The amount of lying snow calculated by a land surface model depends in part
on the amount of snowfall in the meteorological data that are used to
drive the model.
We show that commonly used data sets differ in the amount of snowfall, and
more generally precipitation, over four large Arctic basins.
An independent estimate of the cold-season precipitation is obtained by
combining water balance information from the Gravity Recovery and Climate
Experiment (GRACE) with estimates of evaporation and river discharge and
is generally higher than that estimated by four commonly used meteorological
data sets.
We use the Joint UK Land Environment Simulator (JULES) land surface model
to calculate the snow water equivalent (SWE) over the four basins.
The modelled seasonal maximum SWE is 38 % less than observation-based
estimates on average, and the modelled basin discharge is significantly
underestimated, consistent with the lack of snowfall.
We use the GRACE-derived estimate of precipitation to define per-basin
scale factors
that are applied to the driving data and increase the amount of cold-season
precipitation by 28 % on average.
In turn this increases the modelled seasonal maximum SWE by
30 %, although this is still underestimated compared to observations by
19 % on average.
A correction for the undercatch of precipitation by gauges is compared with the
the GRACE-derived correction. Undercatch correction increases the amount
of cold-season precipitation by 23 % on average, which indicates that some,
but not all, of the
underestimation can be removed by implementing existing undercatch
correction algorithms.
However, even undercatch-corrected data sets contain less precipitation
than the GRACE-derived estimate in some regions, and it is likely that
there are other biases that are not currently accounted for in
gridded meteorological data sets.
This study shows that revised estimates of precipitation can lead to
improved modelling of SWE, but much more modest improvements are found
in modelled river discharge.
By providing methods to better define the precipitation inputs to the
system, the current study paves the way for subsequent work on key
hydrological processes in high-latitude basins.
Introduction
Seasonal snow cover is an important part of the hydrological cycle over a large
part of the Northern Hemisphere, with approximately 45 million km2 of
the land (excluding Greenland) covered by
snow at the seasonal maximum .
Snow plays an important role in the energy, water and biogeochemical cycles of
these areas. The accumulation of water in the snow pack until the spring thaw
dominates the seasonal cycle of runoff and river flow in many
northern river basins ,
and the physical properties of the snow pack,
such as its high albedo, can lead to strong links between the snow pack and the
overlying atmosphere .
Many studies have documented changes in seasonal snow cover in recent decades.
The extent of Northern Hemisphere snow cover decreased through most of the last
century, with a larger rate of decrease since 1970 .
Correlation between changes in spring snow extent and air temperature
reveal the effects of
the snow–albedo feedback .
Observed changes in snow water equivalent (SWE) show more variation between
regions. The seasonal maximum SWE has increased over northern Eurasia over
1966–2010 , but springtime SWE over the mountains of western
North America has decreased over 1960–2002 .
Overall the picture is one of considerable variation depending on
the years and season of analysis, locations and snow measure .
However, declines in snow cover over recent decades are more common than
increases, particularly at warmer or lower locations
see Fig. 4.21 in.
These analyses of historical snow cover have used a variety of data sources,
principally ground-based point observations of precipitation and snow cover
and products derived from satellite data, each of which has strengths and
weaknesses. Direct measurements of snowfall using gauges and of
characteristics of the snow pack (e.g. SWE) are difficult to scale up to large
areas for reasons including the high spatial variability in these quantities
and the sparse network of observing stations e.g..
Inconsistent trends in precipitation and runoff in large northern catchments
confirm that these data are of uncertain quality
. Satellite data can be used for recent
decades, particularly to determine the presence or absence of snow cover.
Passive microwave data from satellites can be used to assess SWE
e.g. Global Snow Monitoring for Climate Research – GlobSnow;, but the need for an algorithm to convert
the measured radiance to SWE coupled with the requirements for supporting data such
as ground-based observations of meteorology introduces uncertainty into the
final estimates.
An alternative to these observation-based methods is to use a land surface
model (LSM) to describe and understand the evolution of the snow pack
e.g.. There are uncertainties associated with LSMs,
and they require large amounts of input data, but they have the advantage of also
being able to estimate future changes in snow cover as they are also used as the
land surface components of the climate models that are used to study future
climates. Simulations using climate models indicate that Northern Hemisphere
snow cover extent will decrease in the future .
Although warming will reduce the length of the snow cover season, precipitation
is predicted to increase over many mid- to high-latitude areas, meaning the
impact of climate change on the amount and distribution of
SWE may differ between regions
. Any such changes in snowfall and snow cover will impact
the wider hydrological cycle in these areas.
If we are to have confidence in these analyses of historical and possible future
conditions from LSMs, we must first establish that they can accurately
represent the snow pack and related processes. To this end community
intercomparison experiments have quantified the performance of land surface
and other models in a variety of settings, some with a focus on snow
processes and others looking at the wider hydrological cycle
e.g.. A recurring theme
across many of these has been the uncertainty that stems from the input
precipitation data, particularly in regions with seasonal snow cover.
This theme is also evident in snow simulations that use the
Joint UK Land Environment Simulator (JULES) land surface
model . noted that seasonal
maximum SWE in pan-Arctic simulations of JULES was often underestimated in comparison
to GlobSnow. This is consistent with the fact that the snowfall specified in
the meteorological data that are used to drive JULES and other LSMs is often
considerably less than SWE estimated by GlobSnow .
suggest that the largest uncertainty in estimates of SWE
from JULES arises from uncertainty in the input precipitation data.
A larger body of research concerns the ability of models to represent other
aspects of land hydrology, particularly runoff and river flow, because
measurements of river flow are generally considered more accurate than those
of catchment-average precipitation and snow pack .
Again a recurring finding of this work is the need to supply the models with
high-quality precipitation data and the uncertainty in these data
.
Furthermore, precipitation data are not only uncertain but possibly
systematically biased low with studies suggesting that insufficient modelled
river flow in high-latitude catchments is a result of insufficient
precipitation in the driving meteorology
.
It should be noted that these model biases are not only limited to land surface
models (those that can be coupled to atmospheric models) but are also shown by
global hydrology models, which typically focus on water resources and
lateral fluxes .
Estimates of precipitation in areas with significant amounts of
snow are made particularly difficult
by the sparsity of the gauge network in many locations, the difficulty of
obtaining an areal average in mountainous terrain, and the need to correct
gauge measurements for undercatch and losses
. Correcting for undercatch requires
information on (at least) the equipment used, wind speed and the phase of precipitation and is itself a difficult and uncertain task
. derived monthly correction
factors for wind-induced undercatch and wetting loss that resulted in a 16 %
increase in December, January and February (DJF) precipitation at the global scale, with much higher values in
some regions. Wind-induced undercatch of solid precipitation was the largest
source of bias. For stations north of 45∘ N, calculated
monthly correction factors of 80 %–120 % of winter precipitation winter due to
snowfall and higher wind speeds. Correcting for orographic effects resulted in
6 % higher global precipitation, 20 % in orographically influenced
areas, using a water balance approach . Although these
correction factors are substantial, they are themselves uncertain and often
only apply to a particular time period and data set, so they cannot necessarily
be used generally. Undercatch correction is not routine to all driving
data sets, and some of these which do not include any correction factors
are widely used e.g. CRUNCEP – Climatic Research Unit Time Series 3.21 correction to National Centers for Atmospheric Prediction/National Center for Atmospheric Research Reanalysis 1;.
Given the difficulties associated with gauge-based data, other products use
information from satellite-based Earth observation or from atmospheric
reanalyses, either alone or in combination with gauge data. However these
approaches involve their own uncertainties, such as how to relate the
measured radiance to a precipitation rate, and the data sets are also
subject to considerable uncertainty .
Given that areal precipitation is difficult to estimate directly using the
aforementioned methods, an alternative approach instead seeks to infer
precipitation from analysis of the total water storage (TWS) of the land
surface as estimated by the Gravity Recovery and Climate Experiment (GRACE)
mission . GRACE
uses gravimetry to assess the changing mass of the surface of Earth, which
can be interpreted as changes in water storage, revealing the dynamics of
TWS at the surface on a monthly timescale. While the
monthly changes in TWS can be observed, GRACE does not indicate what fluxes
or components are the cause of the changes. However, by using the GRACE TWS
anomalies, combined with observed or modelled estimates of river flow and
gridded evaporation fluxes, it is possible to estimate the areal
precipitation .
Previous studies have used GRACE TWS to estimate precipitation inputs
in boreal regions and cold mountainous
basins . These have identified deficiencies or uncertainties
in traditional estimates of precipitation but with a magnitude that varies
between products, locations and times. compared
GRACE-derived
precipitation with two existing precipitation data sets and found a varied
picture, with more variability in North America, while
many precipitation products are significantly lower than the
GRACE-derived estimate in cold mountainous endorheic basins.
compared several raw reanalysis outputs, as well
as some satellite- and gauge-based products, and only found deficiencies in
gauge-based data sets in some regions.
These studies have all used LSM or reanalysis output to provide at
least some of the ancillary data required to calculate precipitation from
GRACE TWS. Since, in this study, the intention is to investigate how changes to
input precipitation can result in changes to the LSM output, independent
estimates of evapotranspiration and river flow have been used in preference to LSM output.
The remainder of this study investigates the hypothesis that many
precipitation data sets that are used to drive land surface models contain
insufficient cold-season precipitation in mid- to high-latitude land areas,
leading to an underestimation of SWE and river flow.
Cold-season precipitation data are assessed against an estimate based on
GRACE TWS data combined with independent estimates of evaporation and basin
discharge. The GRACE-derived precipitation
estimates are used to rescale the cold-season precipitation in the data sets
used to drive the JULES model, and the impacts on SWE and river discharge
are investigated. This is also compared to the effects of undercatch correction.
In Sect. the data sets used to carry out this study are described,
and in Sect. the setup of the JULES model and the experiments
carried out are defined. The method by which precipitation is calculated from
GRACE TWS is described in Sect. . The JULES model runs
are described and evaluated in
Sect. , and the
implications are discussed in Sect. .
Data sets
All analyses in this paper are carried out using data on a regular
latitude–longitude grid at 1∘ resolution. Data with other resolutions
are first regridded to this common grid with a modified version of
SCRIP Spherical Coordinate Remapping and Interpolation;, using the conservative remapping method normalised by
destination area fraction . This study focuses on the four
major Arctic basins – the Ob, Yenisei, Lena and Mackenzie
– which together account for approximately 68 % of the total discharge
into the Arctic Ocean and are marked by
O, Y, L and M respectively in Fig. .
The basins are defined using the 1∘ TRIP (Total Runoff Integrating Pathway) river network data of
. For consistency with the observations of river flow
(see Sect. ), only the part of each basin that drains to
the flow gauging station was considered when calculating spatial averages
of observed or modelled grids. The data sets used all vary in their temporal
coverage; for consistency in analysis a “common overlap period” from 2002 up to and including 2010 was used, during which time all of the driving data sets and
the data sets used for the water balance precipitation estimates were
available. In order to capture full annual cycles of
snow accumulation and melt, annual averages were calculated from 1 September to 31 August of the next year, so the common overlap period contains
8 full years for analysis.
All comparisons were carried out using data from this common overlap period
and averaged over the defined basins, unless explicitly noted otherwise.
Seasonal maximum SWE (mm) for (a) GlobSnow and (b) CMC for the common
overlap period, 2002–2010. The basins used in this study
are labelled with their initial letters (Yenisei, Ob, Lena and
Mackenzie). Grey shows regions that are masked. In both data sets
Greenland is masked. In addition, GlobSnow pixels are masked where
they are more than 50 % “mountain”.
(c) Mean monthly climatology is averaged over the basins (masked using the
GlobSnow mountain mask) for the common overlap
period, 2002–2010. Error bars show the inter-annual variability,
defined as the standard deviation between years.
Meteorological data
A key requirement of land surface models is a time series of meteorological
data. To this end, several “driving” data sets have been created that
represent realistic global climate for the recent past. These are generally
based on global meteorological reanalyses which have been bias-corrected to
observational data sets. They provide a full set of the meteorological
variables required to run an LSM, including air temperature, pressure and
humidity, wind speed, incoming long- and short-wave radiation and the precipitation with which this study is concerned.
Four meteorological data sets were used to drive the JULES model in this study.
These are summarised in Table and described below, with a
focus on the precipitation data. The data were available at 0.5∘ resolution unless stated otherwise. Basin mean monthly climatologies of all
of the driving variables can be seen in Figs. S1 and S2 in the Supplement.
Summary of meteorological driving data.
Driving dataYears availableTemporalSpatialReanalysisSource ofPrecipitationUndercatch(used)resolutionresolutionprecipitationvariablescorrectionCRUNCEP v41901–20106 h0.5∘NCEP/NCARCRU TS 3.21TotalNo(1970–2010)Reanalysis 1PGF v21948–20123 h1∘NCEP/NCARCRU TSTotalNo(1970–2012)Reanalysis 1WFDEI-CRU1979–20163 h0.5∘ERA-InterimCRU TS 3.101Rain and snowYesand 3.21WFDEI-GPCC1979–20133 h0.5∘ERA-InterimGPCC v5 and 6Rain and snowYes
The CRUNCEP v4 data set uses the CRU TS3.21 (Climatic Research Unit Time Series)
monthly data , including gauge-based precipitation, to
bias-correct the NCEP/NCAR (National Centers for Atmospheric Prediction/National Center for Atmospheric Research) Reanalysis 1 .
The reanalysis is spatially interpolated from its original
2.5∘ resolution to the required 0.5∘ resolution with no
adjustment for grid box elevation. No undercatch correction is applied to the
gauge data, and the total precipitation is provided (rather than separate
estimates of rainfall and snowfall).
The Princeton Global Forcing data set v2 PGF; is based on the
NCEP/NCAR Reanalysis 1 with precipitation totals scaled to CRU TS monthly
values and further statistical corrections applied to correct the number
of rain days and to scale to the final 1∘ and 3-hourly resolutions.
Again total precipitation is provided.
The reanalysis is spatially interpolated, including a correction for differences
in grid box elevation. The version used in this study (v2) was not
corrected for undercatch (Justin Sheffield, personal communication, 2018)
contrary to the description of the original data set in .
Two variants of the WATCH (WATer and global CHange) Forcing Data methodology applied to ERA-Interim (ECMWF reanalysis) Data
WFDEI; were used, differing only in the precipitation data
used for bias correction: WFDEI-CRU uses CRU TS 3.1.01 until 2009 and
CRU TS 3.21 from 2010 onwards , while WFDEI-GPCC (Global Precipitation Climatology Centre)
uses GPCC v5 until 2009 and v6 from 2010 onwards and includes many
more stations than CRU. Both variants apply undercatch
corrections and use the ERA-Interim
reanalyses . Variables other than precipitation are
bias-corrected to CRU TS 3.1 until 2009 and CRU TS 3.21 from 2010 onwards
in both versions of WFDEI. The reanalysis was spatially interpolated from
its native resolution (a reduced N128 Gaussian grid, with a resolution of approximately 0.7∘ at the Equator; ) to 0.5∘ resolution, including adjustment for differences in grid box elevation.
Rainfall and snowfall were diagnosed separately in the reanalysis,
with the fraction adjusted appropriately where differences in
grid box elevation resulted in inappropriate precipitation phase.
All of these data sets have been widely used to drive land surface models
in previous studies.
All of them scale precipitation using data from CRU
or GPCC, while an important difference is that only two (WFDEI-CRU and
WFDEI-GPCC) include an undercatch correction.
The undercatch correction implemented in
resulted in an increase in global precipitation of 12 %, with an
increase of 95 % in some boreal regions. Thus this can be an important
source of the underestimation of precipitation.
Although the driving data sets are primarily used by land surface modellers, they
include information from more widely used precipitation data sets, and their
basin-averaged annual precipitation values are similar to those from a range of
other data sets (Fig. S3).
GRACE
The twin GRACE satellites made detailed measurements of changes in Earth's
gravity field from April 2002 to January 2017.
GRACE Tellus used these to provide estimates of the TWS anomaly
relative to the baseline mean over
the years 2004–2009 .
GRACE may be used to study the dynamics of the water cycle on a monthly
time step. Although it may not be used to estimate absolute stores, it
gives an estimate of the integrated water fluxes into and out of a region.
Three methods have been developed to solve the gravity fields using spherical
harmonics (Center for Space Research at University of Texas, Austin – CSR, GeoforschungsZentrum Potsdam – GFZ and Jet Propulsion Laboratory – JPL). These are then used to calculate the gridded TWS,
so that three products are available. As is recommended by
, this study uses the ensemble average of the three
solutions, which effectively reduces the uncertainty in the GRACE product.
The gravimetry measured by the GRACE satellites is converted to a TWS
anomaly, making use of model output. Corrections for
changes in atmospheric pressure are based on ECMWF analysis data,
and corrections for post-glacial rebound are also modelled .
Destriping, Gaussian and degree-60 filters are applied to the gridded data.
In order to correct for signal attenuation introduced by the filters
applied when calculating the grids from the spherical harmonics,
the three TWS land grids must be multiplied by a gridded gain factor,
which is derived by applying the same filters to the output from the
Community Land Model 4 CLM4;.
Although the filters remove a significant component of spatially
correlated and random errors, the gridded GRACE data are not
independent of their neighbours. Uncertainties in the GRACE TWS data are
generally highest at low latitudes and lower towards the poles. In the boreal
regions in this study, estimated measurement error is
around 15 mm . The application of the gridded gain factor
means that the gridded GRACE land data cannot be used to study long-term
trends .
The data used in this study were the GRACE RL05 (Release 05) gridded land
TWS spherical harmonic solutions .
The data are available for the period April 2002 to
January 2017. This study uses the 14 years from September 2002 to
August 2016 so that analyses are carried out over entire cold seasons.
GRDC river discharge
Monthly river discharge data were taken from the Global Runoff Data Centre (GRDC)
Reference Dataset .
For each basin in the current study, the station closest to the river mouth
(as defined in the routing grid; see Sect. ) is used to
define basin discharge.
In this study, basin discharge is quoted in millimetres, for consistency
with the other variables. This is calculated by dividing the total observed
discharge by the area of the 1∘ TRIP river network that drains to the
relevant gauging station .
There are missing observations at each station, and some stations
ceased contributing data before the end of the study period.
Months with missing data were filled using a mean monthly climatology,
calculated using all available data at the station.
Data availability varies between basins, with observations
ending between 2003 and 2012.
A summary of the available and filled data is shown in Table S1 in the Supplement.
GLEAM v3.1a evapotranspiration
The Global Land Evaporation Amsterdam Model v3.1a (GLEAM) provides estimates of
evapotranspiration on a 0.25∘ resolution global grid, for the years
1980–2016. This is a modelled product based on reanalysis and observational
data .
The algorithm uses the Priestley–Taylor equation to calculate
actual evapotranspiration using net surface radiation and near-surface air
temperature. It calculates interception using the Gash model and uses an
adaptation of the Priestley–Taylor equation to calculate evaporation from open
water and sublimation from ice and snow. Inputs include air temperature and
surface radiation from the reanalysis and a gauge-based gridded precipitation
product, along with satellite products for SWE, vegetation properties, and
lightning and soil moisture.
There is a version, v3.1b, which uses satellite
products for air temperature and surface radiation, but this is only available
for latitudes between 50∘ S and 50∘ N and was not suitable for
this study.
Although it is not a direct observation, GLEAM is a well-established estimate
of evapotranspiration, derived from observations as well as reanalysis
products. It has been validated against soil moisture and flux measurements
globally, with GLEAM v3.1a having an unbiased root mean square difference
of 0.7 mm d-1 compared with in situ observations .
While evapotranspiration is low during the cold season, it is still
important to use a robust estimate in order to understand the water balance.
Evapotranspiration is difficult to observe, and any gridded
product is by necessity derived from other observed (or
modelled) quantities .
Snow water equivalentGlobSnow
The GlobSnow project , funded by the European Space Agency (ESA) Data User Element (DUE), provides SWE data for
the years 1979–2016. This study uses GlobSnow v2.0.
It is derived from a combination of satellite microwave radiometer data (sensors of the Scanning Multi-channel Microwave Radiometer – SMMR, Special Sensor Microwave Imager – SSM/I and Special Sensor Microwave Imager/Sounder – SSMIS) and ECMWF weather station observations. A
semi-empirical snow emission model is used to convert passive microwave
emissions to SWE. A data assimilation scheme is then applied to the weather-station- and radiometer-derived SWE observations to produce a map of the
Northern Hemisphere in the Equal-Area Scalable Earth Grid (EASE-Grid) format, at a
nominal 25 km resolution.
Pixels are masked in areas of open water and in areas where there are
mountains.
For this study, the Northern Hemisphere data were regridded onto the analysis
grid. Since the original
data are masked where mountains are present, the regridded data pixels were
masked where at least 50 % of the contributing pixels were mountains.
This mostly affects western North America and some regions in central Asia,
masking 16 % of the area of the Yenisei, 13 % of the Ob,
9 % of the Lena and 14 % of the Mackenzie. In the Eurasian
basins the masked regions are generally
in the south where SWE is low; although in the Mackenzie, the mountains
extend to the north of the basin.
The GlobSnow mountain mask is used for all basin averages of SWE in this study,
both modelled and observed so that comparisons are made over consistent
regions.
CMC Daily Snow Depth Analysis
The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data
are an assimilation of a simple snow accumulation
and melt model, using temperatures and precipitation from the CMC Global
Environmental Multiscale (GEM) forecast model and daily snow depth data
from the World Meteorological Organization (WMO) information system. From
snow depth, SWE is estimated using snow density which is dependent on
month and climate class. The data are provided as a Northern Hemisphere
polar stereographic grid, with a nominal resolution of 24 km, and are
available for the years 1998–2012.
Comparison of SWE products
Figure a and b show the mean seasonal maximum
SWE for GlobSnow and CMC over the common overlap period (2002–2010),
with the season defined to be 1 September–31
August. Seasonal maximum SWE is important for the hydrology of
Arctic river basins, as it is closely related to the amount of water that
is released by the spring melt, which dominates the annual cycle of
basin discharge in these regions.
Many aspects of the large-scale distribution are similar in both
products, and the main maxima are found in similar locations (when not
covered by the GlobSnow mountain mask).
The basin-averaged SWE was calculated by averaging over each basin, after having
applied the GlobSnow mountain mask.
The seasonality of basin-averaged monthly maximum SWE, seen in
Fig. c,
is slightly different between GlobSnow and CMC, with GlobSnow tending
to have a faster accumulation of snow in the earlier part of the winter, while
CMC has a more constant accumulation throughout the winter. However, the seasonal
maxima are consistent between the two data sets, although the GlobSnow maximum
is slightly earlier in the Lena.
This comparison suggests that the GlobSnow and CMC basin-averaged estimates are
remarkably similar for the areas and time periods considered, particularly
for estimates of seasonal maxima. A more
comprehensive comparison of hemisphere-wide estimates by
showed that GlobSnow tended to give larger and earlier seasonal maxima than
CMC over the whole Northern Hemisphere, but this does include regions that are
not being considered in the current study.
The JULES land surface model
This study uses the Joint UK Land Environment Simulator vn4.9
JULES;, which is the land surface component
of the Hadley Centre climate model but is here used in offline mode
driven by near-surface meteorology. We provide brief details here and refer
the reader to the above references for a fuller description.
JULES calculates the exchanges of radiation, heat, water
and carbon between the land surface and the atmosphere.
As employed here each grid box contains a mixture of nine surface types (five
vegetation types and four non-vegetated surfaces), for each of which the surface
fluxes are calculated independently.
Subsurface fluxes of moisture and heat are calculated using a layered
soil model, with a single soil column in each grid box. Supersaturation of soil
moisture is avoided by moving the extra water to lower soil layers.
The snow pack is modelled using a multi-layer snow scheme that employs
a variable number of layers depending on the depth of snow.
Each snow layer has a prognostic temperature, density,
grain size, and solid and liquid water content.
Snowfall in vegetated areas with needle-leaf tree cover
can be partitioned between the vegetation canopy
and the underlying ground .
The intercepted snow leaves the canopy though
sublimation and the wind-speed-dependent unloading of melting snow. The
surface albedo is affected by the evolving grain size of the snow surface
and the extent to which the vegetation is buried by snow. In this study
we employ a TOPMODEL-based (TOPological MODel) parameterisation of surface runoff .
River routing is carried out as a post-processing step, using Total Runoff
Integrating Pathway data on a 1∘ river routing
grid TRIP;.
The ability of JULES to represent snow and other high-latitude phenomena has
been evaluated in several studies including those of ,
, ,
, and .
In particular in SnowMIP2 (Snow Model Intercomparison Project for forest snow processes), JULES
was found to be one of the best models for simulations of open sites,
although it was thus not as skilful at forest sites .
For this study, JULES is run at the native resolution of each driving data
set (0.5∘, except when using the PGF data which is
1.0∘ resolution). Meteorological data are provided
every 6 h (CRUNCEP) or 3 h (all other data), and JULES interpolates these onto a 30 min time step. The precipitation rate is kept constant over
the data time step (3 or 6 h). The model output is regridded to
1.0∘ resolution for analysis.
The WFDEI-CRU and WFDEI-GPCC data sets contain separate rainfall and snowfall
fields that can be used directly by JULES, but the CRUNCEP and PGF data sets
only provide total precipitation. For these, JULES assumes that the
precipitation is snowfall when the near-surface air temperature is less than
or equal to 274 K, while at higher temperatures the precipitation is assumed
to be entirely rainfall. suggest that, as long as the
start and end dates of accumulation are correct, the modelled SWE is
relatively insensitive to the choice of threshold.
The distribution of soil properties and land types was based on data from
the International Geosphere–Biosphere Programme (IGBP) Global Soil Task Force , gridded to the resolution
of each driving data set.
Vertical fluxes of soil water follow Darcy's law, using hydraulic
characteristics calculated after .
Runoff was generated using the TOPMODEL formulation, with the
topographic index regridded from the high-resolution HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales)
data .
The model was spun up using the first 5 years of each data set
four times, for a total of 20 years, and it was then run for several decades starting
in 1970 (CRUNCEP and PGF) or 1979 (both WFDEI variants) until the end of the
available data.
Experiments
Three experiments using JULES were performed:CTL
This signifies control runs, which are driven with the original meteorological data sets.
GRC
This signifies runs in which the total cold-season precipitation
is scaled to match that derived from GRACE, as described in
Sect. below. The scale factors,
which vary by basin and by run, can be seen in
Table . These factors are applied
constantly to both rain and snow for the months October up to and including February. In the rest of the year the precipitation
is unchanged.
UCC
This signifies runs for which wind-based undercatch correction
is applied to the precipitation (CRUNCEP and PGF only).
This uses the same catch ratios as were used in the
creation of WFDEI , one for each month
for rainfall and snowfall separately. The correction is applied throughout
the whole year. The ratios are available at 0.5∘ resolution and
so were regridded to 1∘ resolution for PGF.
Precipitation scale factors, calculated as the ratio of GRACE-derived
to data set precipitation, using all years of overlap between each
driving data set and GRACE.
The full setup for each run is available as a version-controlled Rose suite,
through the Met Office repository:
https://code.metoffice.gov.uk/trac/roses-u (last access: April 2020).
Full details of the suite number and revision for each run is given in Table S2.
Mean bias error
To evaluate the runs, we calculate the mean bias error (MBE) of the annual
maximum SWE in each basin (Mb) to be
Mb=1ny∑i=1nySi,b-si,b,
where Si,b is the modelled SWE in basin b at time step i,
si,b is the corresponding observed SWE, and ny is the number
of years of overlap between the model and observations. To obtain the
average over basins MS, the area-weighted mean of the basins is calculated as
MS=∑b=1nbAbMb∑b=1nbAb,
where Ab is the total area of basin b.
The variance of the bias error in each basin is
σb2=1ny-1∑i=1nySi,b-si,b2,
and the combined variance is
σS2=n-1∑b=1nbAbσb2∑b=1nbAb-1.
A water balance approach was used to estimate the
monthly precipitation (P; mm month-1) at a basin scale:
P=dStotdt+E+Qnet,
where dStot/dt (mm month-1)
is the change in TWS (Stot; mm), E (mm month-1) is
the total evaporation flux, including sublimation, and Qnet
(mm month-1) is
net runoff. At the annual or monthly scale, this can be calculated to be
∫t1t2P(t)dt=Stot(t2)-Stot(t1)+∫t1t2E(t)+Qnetdt,
where t1 and t2 are the start and end of each accumulation period
respectively.
TWS encompasses all water storage, including soil moisture; groundwater; water in wetlands, lakes and rivers; and water stored as snow in the
snow pack. Evaporation includes transpiration, evaporation from soil surfaces,
evaporation from intercepted water and other open water (rivers and lakes) and sublimation from frozen surfaces.
As the water balance is calculated over whole basins there is no incoming
runoff, and the net runoff is equal to the basin discharge.
The change in TWS is calculated by differencing the GRACE anomalies between
months and averaging over the basin. Monthly evaporation is provided by
GLEAM and averaged over the basin, while the basin discharge is obtained
from the GRDC measurement station closest to the basin outflow.
The water balance is calculated for each month in the cold season
(October up to and including February). This is defined
based on two metrics: (a) the change in GRACE TWS must be positive and
(b) the evaporation must be small. There is inter-annual variability in the
length of this season, so the period October to February was chosen as
a conservative accumulation period that would apply over all years and basins.
During this season snowfall is
the dominant precipitation type; the accumulating snow pack is the dominant
change to TWS; and the runoff and evaporation fluxes are
relatively small (Fig. S4). This minimises the effect of
uncertainty in evaporation and basin discharge products,
which could be more significant outside of the cold
season .
Comparison of precipitation estimates
Figure shows the long-term mean monthly accumulated total
precipitation during the cold season (October–February) for each of the four
driving data sets and for the GRACE-derived precipitation estimates (all
averaged over the common overlap period).
The differences between data sets vary between the catchments, but in general
the driving data sets accumulate less precipitation than the GRACE estimate.
The CRUNCEP and PGF data sets have lower precipitation than the WFDEI data through
the whole cold season in all basins but are very similar to each other, as they were both
bias-corrected to CRU data without undercatch correction.
The WFDEI data sets generally have higher precipitation and are much
closer to the GRACE-derived estimate, even indicating a larger
accumulation by December in the Lena and the Ob. The final accumulation is
similar to the GRACE-derived estimate in three basins for WFDEI-GPCC and
two basins for WFDEI-CRU .
Overall the implication is that the driving data sets often do not contain
enough precipitation to close the water budget in these basins in the cold
season, particularly for the CRUNCEP and PGF data.
Mean monthly accumulated cold-season precipitation from
October up to and including February, averaged over the common overlap period, 2002–2010, as
derived from GRACE TWS (Sect. ) and from the
driving data sets (Sect. ).
Calculation of precipitation scale factors
In order to appropriately increase the amount of precipitation in the
driving data sets, a scale factor was calculated for each data set in each
study basin.
The scale factor between GRACE and each driving data set was found by
calculating the ratio of cold-season accumulated precipitation
derived from GRACE to that in the driving data. As the driving data cover a
longer time period than GRACE, to ensure that the difference is not due to
inter-annual variability or long-term climate trends, each ratio was calculated
using only data from the
overlapping period of the relevant data set with GRACE. This period starts in
2002 for all of the data sets and runs to the end of each data set.
The scale factors for each basin can be seen in
Table .
The relative scaling between basins is consistent for each data set,
with the Yenisei requiring the largest correction (between a 24 % and 55 %
increase) and the Ob requiring the least (between a 2 % decrease and 37 %
increase).
There is a striking difference between the
WFDEI data sets, which require increases up to 27 % (and one decrease), and
the PGF and CRUNCEP data sets, which require much larger increases of between
35 % and 55 %.
The GRC runs of JULES were carried out by scaling
both snowfall and rainfall by these factors during the cold season only
for all years of the run (not just the GRACE period). Snow and rain that falls
outside of this season were unchanged. This design ensured that each
run received the same amount of cold-season precipitation as indicated
by GRACE, when averaged over the basin and over the period of overlap between
the driving data set and GRACE; the temporal and
spatial variability of precipitation still varied between runs.
JULES model runsControl runs (CTL)
The simulated monthly maximum SWE for each basin can be seen in
Fig. .
In many cases JULES accumulates snow more slowly than the observational
estimates, particularly later in the accumulation season, and the
SWE is less sharply peaked than the observations. This is possibly due to
particular biases in the driving data (perhaps missing smaller events
towards the end of the accumulation season) or in how the model responds
(perhaps starting to melt too early).
Climatology of monthly maximum SWE (mm) from observations and from the
CTL model runs averaged over the common overlap period, 2002–2010.
Figure shows the MBE for seasonal
maximum SWE averaged over all four basins with respect to
both GlobSnow and CMC.
In general the magnitude of the MBE is largest for PGF
and smallest for WFDEI-GPCC (Fig. S5 shows the MBE for each basin).
The largest SWE deficit is found when using the PGF data in the Lena basin, for which
the bias is -57 % compared to GlobSnow and -56 % compared to CMC. The smallest deficit
is found in the Mackenzie using the WFDEI-GPCC driving data, which have
an MBE of -17 % compared with both GlobSnow and CMC.
Maps of the modelled SWE show broadly similar distributions of snow to the
observations (Fig. S6). The difference between the modelled and observed SWE
(e.g. GlobSnow in Fig. S7) show that, although there are a few regions of higher
SWE in JULES
outside of the studied basins, there is a deficit of modelled lying snow across
most of the Northern Hemisphere.
The general tendency for JULES
to underestimate SWE is consistent with the indication of insufficient
cold-season precipitation (Fig. ), and both analyses
also agree that the WFDEI runs are generally closer to the observations.
However it is clear that, even in the cases where the precipitation is
close to the GRACE-derived estimate, the modelled maximum SWE is still lower
than the observed SWE.
Mean bias error (MBE) of seasonal maximum SWE (mm), averaged over all
basins (Eq. ) and calculated over the common overlap period, 2002–2010. The error bars show
the basin-averaged standard deviation, σS (Eq. ).
The annual discharge in JULES is also
severely underestimated compared with GRDC observations, as seen in
Fig. , which shows the basin discharge averaged over all four
basins.
All of the runs demonstrate a substantial dry bias in
comparison with GRDC values. CRUNCEP is particularly poor, with an 80 %–90 %
deficit in annual mean basin discharge, while the other runs underestimate
basin discharge by between 52 % and 57 % on average. This difference between
CRUNCEP and the other data sets is consistent across all the basins.
The deficit is particularly pronounced
during the spring peak (Fig. S9), which in nature is strongly driven by
snowmelt. In JULES, the peak in the modelled river flow is broader and
occurs later than the observations, which may indicate
deficiencies in the modelled hydrology as well as a lack of melting snow.
However, at the annual scale the bias may also be affected by
warm-season rainfall.
Mean annual observed and modelled basin discharge (mm yr-1) for the
common overlap period, 2002–2010.
Runs with scaled precipitation (GRC)
Using the GRACE-scaled precipitation to drive JULES gives an improved SWE
representation overall, with
significant increases in monthly and seasonal maximum SWE
(Fig. ). The modelled seasonal cycle is much more similar
to observations, particularly in the Yenisei and Mackenzie and early in the
season for the Lena and Ob. In the Ob, the CRUNCEP GRC run is much more similar
to the observations than any of the other models, while in the Mackenzie
it now overestimates SWE. In the Lena, the seasonal
peak is still not attained with any data set.
Overall the largest increases in SWE are found for the CRUNCEP and PGF
runs, consistent with the larger scale factors used
(Table ). The annual maximum SWE is
increased by 30 % averaged across all basins and runs,
with the CRUNCEP and PGF runs having an average increase
of 48 % and the WFDEI runs having a smaller average increase of 12 %.
Overall, the magnitude of the MBE is decreased for all driving data sets
(Fig. ).
Although there is an increase in SWE over the extent of each basin (Fig. S8),
the effect on the MBE varies between driving data sets and basins:
the widespread underestimation of the CTL runs is replaced by a more nuanced
picture, including some cases with a modest overestimation of SWE (Fig. S5).
The modelled estimates of maximum SWE in the Yenisei and Mackenzie are now
clustered around the observational estimates, while a
low bias is still found in most runs for the Lena and Ob.
The biggest improvement is seen in CRUNCEP runs, which now only has an
MBE of -5 % overall.
Climatology of monthly maximum SWE (mm) from observations and from the
GRC model runs for the common overlap period, 2002–2010.
Basin discharge is higher in the scaled runs than the control runs, although
the increase is relatively small (27 % overall) compared to the general dry
bias (Fig. ). The improvement in representation of basin discharge varies significantly
between data sets, from 8 % in WFDEI-GPCC to 57 % in CRUNCEP, which still has
the lowest basin discharge (the highest absolute increase is 27 mm yr-1 in PGF,
while the lowest is 7 mm yr-1 in WFDEI-GPCC).
Although the basin discharge increases overall, the increase in SWE and the
resultant increase in spring snowmelt do not change the timing of the
peak basin discharge in the models.
The modelled estimates of basin discharge are still
considerably lower than the GRDC estimates, with CRUNCEP underestimating by
74 % and the others between 39 % and 52 %.
This implies that factors other
than cold-season precipitation and snow accumulation are contributing to the
lack of river flow. In particular the low bias and late peak in
discharge for the Yenisei and
Mackenzie persists (Fig. S9) despite the peak SWE now being
close to the observational estimates.
Runs with undercatch correction (UCC)
As discussed above, a possible explanation for underestimation of
cold-season precipitation in observation-based data sets is undercatch.
In an attempt to account for this effect two of the data sets –
WFDEI-CRU and WFDEI-GPCC –
already implement undercatch correction. The two data sets that do not apply a
correction required the largest scaling factors to match the GRACE-derived
estimates. To investigate the importance of the undercatch correction in
comparison to the GRACE corrections, the catch ratios calculated by
were used to correct the CRUNCEP and PGF data on a monthly
basis. Both rainfall and snowfall were adjusted, using separate catch ratios
for all months of the year, and used to force JULES in the UCC runs.
The undercatch correction increases the precipitation across the whole year,
but here we focus on the cold-season precipitation increase. The increase
is large but less than indicated by the GRACE precipitation scale factors.
In the Ob, undercatch correction increases
the amount of cold-season precipitation by 26 % over the whole basin, compared
to an overall increase of
35 % in the GRC runs for CRUNCEP and PGF. However, in the other basins
the increase in precipitation due to undercatch correction is between
20 % and 25 %, and the increase based on GRACE precipitation is between
45 % and 55 %. Thus, the undercatch correction accounts for between 30 % and 75 %
of the cold-season deficit that is indicated by the comparison with the
GRACE-derived precipitation estimates.
The improvement to the estimate of SWE is clear, although it is not as large as
in the GRC runs (Figs. and ).
On average
the two undercatch-corrected driving data sets used for the UCC runs have a
maximum SWE increased by 34 %, compared to an increase of 48 % when the same
two data sets were scaled by the GRACE scale factors.
In contrast, the increases in annual basin discharge were much more similar
between the GRC and UCC runs. In the Ob, the undercatch correction even
resulted in higher basin discharge than the GRACE scaling, despite the
GRACE scaling resulting in higher total annual precipitation;
this is because in the UCC runs the precipitation is increased across
the whole year, contributing to increased river flow in warm months as well as
the spring snowmelt,
whereas the GRC runs only have increased precipitation in the
cold season.
Along with the fact that GRACE indicates that the WFDEI runs
also underrepresent precipitation in at least some of these basins,
this suggests that although undercatch correction can account for some of the
deficit in precipitation, it cannot account for all of it.
Discussion
This study has shown that gauge- or reanalysis-based estimates of cold-season
precipitation in boreal basins can be significantly lower than is suggested by
a water balance approach using GRACE TWS data. By scaling the
driving meteorological data sets, we can significantly improve the
representation of lying snow in a land surface model.
The deficits seen in precipitation are large but similar in magnitude to those
calculated by , who calculated precipitation corrections of
between 36 % and 70 %, based on a comparison of modelled and observed SWE, and by
, who used GRACE to evaluate precipitation data sets in
endorheic cold mountainous basins, and found that some precipitation data sets
only captured between 10 % and 60 % of the GRACE-based precipitation estimates.
In this study we used time-invariant corrections, but this could
be extended to allow time-varying corrections, say on an annual basis.
The GRACE-based estimates provide a means to account for
measurement errors in the gauge data that are used to bias-correct
reanalyses. This study suggests that undercatch is an important source of
error, but in many cases the application of an undercatch correction does
not remove the bias entirely. Further sources of error in the gauge-based
estimates include spatial variability that is missed by the gauge network.
While the GRACE TWS method used here circumvents many of the limitations
of the gauge-based correction, it introduces uncertainties from other terms
in the water budget (e.g. evaporation). It is also inherently large scale and
so improves the basin-averaged SWE, but it may not result in improved SWE when looking at the local scale.
Undercatch correction, on the other hand, can be calculated at a local or
grid box scale and can, in theory at least, more easily take account of
changing meteorological conditions down to the timescale of individual storms.
Furthermore it is easy to implement and is attractive, as it addresses
known deficiencies in the observations. It can also be used to correct
historical data sets that predate GRACE.
However the correction is uncertain, including dependency on the type of
gauge used in each area, and again it requires further inputs, each with
uncertainties. A further approach, not studied here, is to use estimates of
SWE, either from ground measurements or remote sensing, to estimate snowfall.
This study of cold-season processes shows that the undercatch correction is
equivalent to a substantial fraction of the GRACE-derived correction, suggesting
that for gauge-based data sets the undercatch correction can be considered a
minimum requirement that should be applied whenever possible.
However, this varies by region, implying that there are different reasons
for the deficit in precipitation in different regions. The CRUNCEP
and PGF data sets do not include an undercatch correction but have been widely
used with land surface models; our results suggest that any aspects of those
studies that are potentially sensitive to snowfall, such as high-latitude
hydrological analyses, should be regarded as particularly uncertain.
Undercatch is a known issue for rainfall as well as snowfall
and is considered in undercatch correction algorithms from and .
The annual water balance calculated from GRDC, GLEAM and GRACE
suggests that there is insufficient precipitation input in each of these
basins to support the observed river flow (Fig. S10).
Therefore undercatch errors potentially also
affect summertime precipitation.
Climatology of monthly maximum SWE (mm) from observations and from the
UCC model runs for the common overlap period, 2002–2010.
Figure shows that the increase in seasonal maximum SWE is
proportional to the increase in seasonal snowfall between experiments, with a very strong
correlation (r2=0.98) between the two. The gradient of 0.76
implies that not all of the increase
in snowfall manifests as an increase in SWE,
which is mainly due to increased
sublimation and a small increase in snowmelt,
but it does confirm that correcting snowfall is a direct and
reliable approach with which to target errors in simulated SWE.
This correlation also implies that the approach of
to increase driving snowfall based on the
required increase in SWE is reasonable.
Increase in annual maximum SWE (mm) from CTL to GRC and UCC runs
compared to an increase in annual snowfall (mm yr-1), averaged over all basins
(using the GlobSnow mountain mask for snowfall as well as SWE)
for each driving
data set. This is averaged over the whole of each JULES run
(see Table ).
The use of corrected precipitation improved results in most catchments
and with most driving data sets, often substantially. In contrast, the
larger dry bias in modelled basin discharge was found in all simulations, with
only modest improvements from corrected precipitation. This difference
is a result of the much closer links between point snowfall and SWE, whereas
modelled discharge can be viewed as the net effect of many hydrological
processes acting across the catchment. A striking illustration of this
is the large difference between the CRUNCEP and PGF river discharge,
despite very similar precipitation inputs (both having been bias-corrected
to CRU precipitation). The larger river flow in PGF is balanced by
a smaller evapotranspiration flux (Fig. S11), while evapotranspiration
in CRUNCEP tends to be closer to the WFDEI-based estimates.
In turn this can be related to differences in other aspects of the
meteorological forcing (Figs. S1 and S2);
in particular, PGF has much higher specific humidity,
particularly in the summer, than the other data sets.
In spring and summer PGF humidity is 18 %–87 % higher than in CRUNCEP,
which suppresses evapotranspiration, which is 10 %–17 % lower in PGF for the CTL runs.
(This can be contrasted with the air temperature, which is nearly identical
in PGF and CRUNCEP, implying that the data sets employed different methods to
reconstruct humidity.)
This is consistent with the fact that evapotranspiration in the PGF runs is
generally less than estimated by GLEAM, while the other runs tend to be closer
to GLEAM, although there is considerable uncertainty in the GLEAM product.
Although the focus of this study is on the correction of precipitation products,
which is shown to improve modelled SWE, it is clear that other
meteorological variables are also important, particularly for other
aspects of modelled hydrology.
The modelled runoff ratio (Qnet/P) is much lower on average for CRUNCEP
than for runs with the other data sets. When combined with the already relatively
low precipitation in the CTL run, this means the CRUNCEP data set generates
particularly small values of discharge in all basins; overall the CRUNCEP
basin discharge is 16 % of observed discharge in the CTL run and 25 % of observed discharge in the
GRC and UCC runs.
This is related to the longer time step of the data set (6 h rather
than 3 h), within which the precipitation rate is constant in the JULES
runs; the lower intensity of precipitation tends to result in larger
interception loss, and higher interception loss reduces water available
for runoff.
For CRUNCEP, the interception is the largest component of evapotranspiration
(interception is 35 % of total evapotranspiration in the CTL run,
whereas transpiration is 28 %), while the other driving data sets have lower
interception losses (22 %–24 % for CTL), with transpiration as the largest
component instead (35 %–38 % of total evapotranspiration for CTL).
The extra precipitation input in the GRC runs largely
appears as increased evaporation in CRUNCEP runs, whereas it is largely
converted to runoff for the other data sets (Fig. S11). This means that, even
with a significant increase in precipitation in the CRUNCEP runs, the
basin discharge is still extremely underestimated.
The seasonal maximum SWE tends to be biased low even when precipitation
inputs are corrected. Assuming that both the precipitation and SWE
observations are now correct, this points to structural uncertainties of the model as
a possible remaining source of bias.
One possibility is that the modelled
sublimation rate is too high. Sublimation in the CTL runs, averaged over all
basins, ranged from 37 % of snowfall (PGF) to 44 % (CRUNCEP).
It is very difficult to measure sublimation,
particularly over large areas, and most estimates tend to be based on water
balance methods or models . Estimates at a range of scales
indicate that 10 %–50 % of snowfall can
be lost through sublimation, with substantial variation depending on land cover
and meteorological conditions e.g..
For the Mackenzie basin sublimation was estimated as 29 mm
7 % of annual precipitation;,
while numerical modelling by suggested that approximately
24 % of annual snowfall in the area 50–70∘ N was lost as sublimation.
Thus the evidence suggests that sublimation might be rather high in JULES,
although the evidence base is itself rather uncertain. Further investigation,
including ensembles of LSMs and global hydrological models (GHMs), and
point-scale runs in data-rich environments to examine the simulated
water budget across a range of land covers such as in
SnowMIP2
will allow us to identify the role of model uncertainty and
increase our confidence in the ability of JULES and other models to
correctly simulate sublimation and other key processes.
However, errors in modelled sublimation and indeed all cold-season processes
are insufficient to fully account for the dry bias in annual discharge.
It is also possible that rainfall outside of the cold season is
underestimated in the driving data. In all of these basins there is more
precipitation in summer (when it is more likely to be rain) than in winter,
suggesting that correction of warm-season rainfall could potentially
have a larger impact on the annual water balance. A sensitivity test, applying
the cold-season scaling factors to the whole year, suggests that this can
double the modelled basin discharge, significantly decreasing the
underestimation (Fig. S12). However, there are clear
signs that meteorological inputs are not the only source of error and that
there are fundamental deficiencies in the model's representation of runoff
generation processes: even a good estimate of peak SWE does not result in
a good representation of the spring discharge peak (Fig. S9).
It is likely that the parameterisation of infiltration into partly frozen
ground and related runoff generation processes are not well represented
in JULES. Previous work has shown that alternative descriptions of frozen soil
can improve the modelled runoff peak .
Conclusions
There is a substantial body of literature on the intercomparison of global
precipitation data sets, with a lesser focus on the particular issues found at
high latitudes where much of the precipitation falls as snow. There is an
ongoing need to compare these precipitation products and to ensure that the
best meteorological data are made available as inputs to land surface
modelling. This study has focussed on precipitation, but the model results
clearly indicate that other variables, such as humidity, are
also important.
Land surface modellers should continue to critically evaluate the
meteorological data they use and ideally run a model using a variety of
data sets. The extent to which results are sensitive to the choice of
meteorological data will vary; for some analyses there might be
relatively little sensitivity, but by and large this can only be determined
through the use of an ensemble of runs forced by alternative data.
Meteorological data should also be evaluated in combination with products that
describe related parts of the hydrological system, such as GRACE TWS; estimates
of SWE based on remote sensing; and river discharge as used in this study.
Although each product comes with its own uncertainty and a range of
alternative and potentially conflicting data sets is often available, the
combination of estimates across different parts of the hydrological system
can provide extra insights, particularly if a model shows consistent biases
across several components.
Even with the scaled precipitation data sets, there are still remaining
biases in the model output, which vary across basin and data set. Using
scaled precipitation data sets would allow for the further identification of biases
due to model uncertainty.
This study shows that at a basin scale, the cold-season precipitation in four
data sets that are commonly used to drive land surface models is low compared
with estimates derived from GRACE TWS.
This leads to consistent and large errors in SWE and basin discharge
calculated by JULES, which are also low compared to observations.
By increasing the precipitation in JULES to match
the GRACE estimates, the modelled SWE is substantially improved, although river
discharge is still low – likely because of a combination of poor modelling of
runoff processes during the spring melt and possible underestimation of
summertime precipitation. By providing methods
to better define the precipitation inputs to the system, the current study
paves the way for subsequent work on key hydrological processes.
Code availability
The model runs in this paper were carried out using
JULES vn4.9, with modifications to allow scaling of the precipitation
input. This is available through the JULES repository and is located at
https://code.metoffice.gov.uk/trac/jules/browser/main/branches/test/emmarobinson/vn4.9_arctic_scaling?rev=9936 (last access: April 2020)
(registration required).
The runs were carried out using Rose (http://metomi.github.io/rose, last access: April 2020); the
control files are available through the Met Office Rose repository
(https://code.metoffice.gov.uk/trac/roses-u, last access: April 2020); and details are in Table S2.
All other analysis code, including river routing and post-processing, is
available on request.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-24-1763-2020-supplement.
Author contributions
DBC designed the study. ELR carried out model runs and the analysis. Both wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank the editor and the reviewers for their valuable feedback and comments.
GRACE land data are available
at http://grace.jpl.nasa.gov (last access: April 2020) and are supported by the NASA MEaSUREs Program.
Financial support
This work has been supported by the Natural Environment Research Council
award number NE/H000224/1 and award number NE/S017380/1 as part of
the Hydro-JULES programme.
Review statement
This paper was edited by Bob Su and reviewed by two anonymous referees.
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