Precipitation Biases Research Articles

Inter-product biases in global precipitation extremes

Biases in climatological and extreme precipitation estimates are assessed for 11 global observational datasets constructed with merged satellite measurements and/or rain gauge networks. Specifically, the biases in extreme precipitation are contrasted with mean-state biases. Extreme precipitation is defined by a 99th percentile threshold (R99p) on a daily, 1° × 1° grid for 50 °S–50 °N. The spatial pattern of extreme precipitation lacks distinct features such as the ITCZ that is evident in the global climatological map, and the climatology and extremes share little in common in terms of the spatial characteristics of inter-product biases. The time series also exhibit a larger spread in the extremes than in the climatology. Further, when analysed from 2001 to 2013, they show relatively consistent decadal stability in the climatology over ocean while the dispersion is larger for the extremes over ocean. This contrast is not observed over land. Overall, the results suggest that the inter-product biases apparent in the climatology are a poor predictor of the extreme-precipitation biases even in a qualitative sense.

Extratropical–Tropical Interaction Model Intercomparison Project (Etin-Mip): Protocol and Initial Results

AbstractThis article introduces the Extratropical–Tropical Interaction Model Intercomparison Project (ETIN-MIP), where a set of fully coupled model experiments are designed to examine the sources of longstanding tropical precipitation biases in climate models. In particular, we reduce insolation over three targeted latitudinal bands of persistent model biases: the southern extratropics, the southern tropics, and the northern extratropics. To address the effect of regional energy bias corrections on the mean distribution of tropical precipitation, such as the double intertropical convergence zone problem, we evaluate the quasi-equilibrium response of the climate system corresponding to a 50-yr period after the 100 years of prescribed energy perturbation. Initial results show that, despite a large intermodel spread in each perturbation experiment due to differences in ocean heat uptake response and climate feedbacks across models, the southern tropics is most efficient at driving a meridional shift of tropical precipitation. In contrast, the extratropical energy perturbations are effectively damped by anomalous heat uptake over the subpolar oceans, thereby inducing a smaller meridional shift of tropical precipitation compared with the tropical energy perturbations. The ETIN-MIP experiments allow us to investigate the global implications of regional energy bias corrections, providing a route to guide the practice of model development, with implications for understanding dynamical responses to anthropogenic climate change and geoengineering.

Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class

AbstractA decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

The DOE E3SM Coupled Model Version 1: Description and Results at High Resolution

AbstractThis study provides an overview of the coupled high‐resolution Version 1 of the Energy Exascale Earth System Model (E3SMv1) and documents the characteristics of a 50‐year‐long high‐resolution control simulation with time‐invariant 1950 forcings following the HighResMIP protocol. In terms of global root‐mean‐squared error metrics, this high‐resolution simulation is generally superior to results from the low‐resolution configuration of E3SMv1 (due to resolution, tuning changes, and possibly initialization procedure) and compares favorably to models in the CMIP5 ensemble. Ocean and sea ice simulation is particularly improved, due to better resolution of bathymetry, the ability to capture more variability and extremes in winds and currents, and the ability to resolve mesoscale ocean eddies. The largest improvement in this regard is an ice‐free Labrador Sea, which is a major problem at low resolution. Interestingly, several features found to improve with resolution in previous studies are insensitive to resolution or even degrade in E3SMv1. Most notable in this regard are warm bias and associated stratocumulus deficiency in eastern subtropical oceans and lack of improvement in El Niño. Another major finding of this study is that resolution increase had negligible impact on climate sensitivity (measured by net feedback determined through uniform +4K prescribed sea surface temperature increase) and aerosol sensitivity. Cloud response to resolution increase consisted of very minor decrease at all levels. Large‐scale patterns of precipitation bias were also relatively unaffected by grid spacing.

WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).

Evaluation of New CORDEX Simulations Using an Updated Köppen–Trewartha Climate Classification

A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about ± 0.5 K) with low spatial standard deviation (about ± 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below ± 50 % in most domains; however, spatial standard deviation varies from ± 30 % to ± 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

Characterizing the North American Monsoon in the Community Atmosphere Model: Sensitivity to Resolution and Topography

AbstractThis work examines the effect of horizontal resolution and topography on the North American monsoon (NAM) in experiments with an atmospheric general circulation model. Observations are used to evaluate the fidelity of the representation of the monsoon in simulations from the Community Atmosphere Model version 5 (CAM5) with a standard 1.0° grid spacing and a high-resolution 0.25° grid spacing. The simulated monsoon has some realistic features, but both configurations also show precipitation biases. The default 1.0° grid spacing configuration simulates a monsoon with an annual cycle and intensity of precipitation within the observational range, but the monsoon begins and ends too gradually and does not reach far enough north. This study shows that the improved representation of topography in the high-resolution (0.25° grid spacing) configuration improves the regional circulation and therefore some aspects of the simulated monsoon compared to the 1.0° counterpart. At higher resolution, CAM5 simulates a stronger low pressure center over the American Southwest, with more realistic low-level wind flow than in the 1.0° configuration. As a result, the monsoon precipitation increases as does the amplitude of the annual cycle of precipitation. A moisture analysis sheds light on the monsoon dynamics, indicating that changes in the advection of enthalpy and moist static energy drive the differences between monsoon precipitation in CAM5 1.0° compared to the 0.25° configuration. Additional simulations confirm that these improvements are mainly due to the topographic influence on the low-level flow through the Gulf of California, and not only the increase in horizontal resolution.

Revisiting Sensitivity to Horizontal Grid Spacing in Convection-Allowing Models over the Central and Eastern United States

Abstract Hourly accumulated precipitation forecasts from deterministic convection-allowing numerical weather prediction models with 3- and 1-km horizontal grid spacing were evaluated over 497 forecasts between 2010 and 2017 over the central and eastern conterminous United States (CONUS). While precipitation biases varied geographically and seasonally, 1-km model climatologies of precipitation generally aligned better with those observed than 3-km climatologies. Additionally, during the cool season and spring, when large-scale forcing was strong and precipitation entities were large, 1-km forecasts were more skillful than 3-km forecasts, particularly over southern portions of the CONUS where instability was greatest. Conversely, during summertime, when synoptic-scale forcing was weak and precipitation entities were small, 3- and 1-km forecasts had similar skill. These collective results differ substantially from previous work finding 4-km forecasts had comparable springtime precipitation forecast skill as 1- or 2-km forecasts over the central–eastern CONUS. Additional analyses and experiments suggest the greater benefits of 1-km forecasts documented here could be related to higher-quality initial conditions than in prior studies. However, further research is needed to confirm this hypothesis.

Deriving Bias and Uncertainty in MERRA-2 Snowfall Precipitation Over High Mountain Asia

A Bayesian approach to estimate bias and uncertainty in snowfall precipitation from MERRA-2 and other precipitation products was applied over High Mountain Asia (HMA), using a newly developed snow reanalysis method. Starting from an ‘uninformed’ prior probability distribution, a posterior scaling factor applied to MERRA-2 snowfall was derived by constraining model-based estimates of seasonal snow accumulation and ablation over the water year (WY) with fractional snow covered area (fSCA) measurements derived from Landsat and MODIS (MODSCAG). Several sub-domains (nine representative 1° by 1° tiles) across HMA were examined over the period WYs 2001-2015 and compiled into an uncertainty parameterization where a lognormal distribution was fitted to the empirical posterior distribution with a mean of 1.54 (median of 1.19) and coefficient of variation of 0.83, indicating that MERRA-2 underestimates snowfall on average by ~54% with sizeable uncertainty. For reference, the uncertainties in snowfall precipitation from the ERA5 and APHRODITE-2 precipitation produces were also evaluated, and these products were found to underestimate snowfall, on average by a factor around 1.78 and 3.34 (with median scaling factors of 1.42 and 2.51) respectively. The results indicate that snowfall precipitation at high-elevations dominated by snowfall is underestimated in most existing products, especially in the gauge-based APHRODITE-2 product, where the biases were also found to exhibit geographical variations with the largest underestimation in monsoon-influenced high-elevation tiles. The derived MERRA-2 uncertainty model is being used to develop a full domain-wide HMA snow reanalysis, which will shed further light onto the space-time variations in snowfall biases in these products.

How can CMIP5 AGCMs’ resolution influence precipitation in mountain areas: the Hengduan Mountains?

The precipitation over the Hengduan Mountains (HMs) during rainy seasons is investigated based on observations, reanalysis datasets, and 28 atmospheric general circulation models (AGCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5). Most CMIP5 AGCMs generally capture two observed precipitation centers over the southwestern HMs and on the west side of Sichuan basin (WSSB), but their location, range, and magnitude vary with models. As the horizontal resolution increases, the details of simulated precipitation pattern are improved and closer to observation and reanalysis, as well as the increasing magnitude of precipitation over the southwestern HMs. However, the simulated precipitation on the WSSB is overestimated regardless of resolution. Mechanisms involved in resolution affecting precipitation pattern and biases of precipitation on the WSSB are explored. Representation of topography in AGCMs influences orographic effect, which contributes to simulations of both horizontal and vertical moisture flux convergence and further precipitation over the HMs. The biases of WSSB precipitation between reanalysis and AGCMs are attributed to the discrepancy in the vertical distribution of upward motions. The simulated upward motions can reach a higher level than reanalysis, and a spurious center of upward motions develops at 400 hPa due to the overestimation of circulation-precipitation feedback in AGCMs.

Sensitivity of Ozone Dry Deposition to Ecosystem‐Atmosphere Interactions: A Critical Appraisal of Observations and Simulations

AbstractThe response of ozone (O3) dry deposition to ecosystem‐atmosphere interactions is poorly understood but is central to determining the potential for extreme pollution events under current and future climate conditions. Using observations and an interactive dry deposition scheme within two dynamic vegetation land models (Geophysical Fluid Dynamics Laboratory LM3.0/LM4.0) driven by observation‐based meteorological forcings over 1948–2014, we investigate the factors controlling seasonal and interannual variability (IAV) in O3 deposition velocities (Vd,O3). Stomatal activity in this scheme is determined mechanistically, depending on phenology, soil moisture, vapor pressure deficit, and CO2 concentration. Soil moisture plays a key role in modulating the observed and simulated Vd,O3 seasonal changes over evergreen forests in Mediterranean Europe, South Asia, and the Amazon. Analysis of multiyear observations at forest sites in Europe and North America reveals drought stress to reduce Vd,O3 by ~50%. Both LM3.0 and LM4.0 capture the observed Vd,O3 decreases due to drought; however, IAV is weaker by a factor of 2 in LM3.0 coupled to atmospheric models, particularly in regions with large precipitation biases. IAV in summertime Vd,O3 to forests, driven primarily by the stomatal pathway, is largest (15–35%) in semiarid regions of western Europe, eastern North America, and northeastern China. Monthly mean Vd,O3 for the highest year is 2 to 4 times that of the lowest, with significant implications for surface O3 variability and extreme events. Using Vd,O3 from LM4.0 in an atmospheric chemistry model improves the simulation of surface O3 abundance and spatial variability (reduces mean biases by ~10 ppb) relative to the widely used Wesely scheme.

Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica

Global tree cover products are widely used in analyses of deforestation, fragmentation, and connectivity, but are rarely critically assessed. Inaccuracies in these products could have consequences for future decision making, especially in data-poor regions like the tropics. In this study, potential biases in global and regional tree cover products were assessed across a diverse tropical country, Costa Rica. Two global tree cover products and one regional national forest cover map were evaluated along biophysical gradients in elevation, precipitation, and agricultural land cover. To quantify product accuracy and bias, freely available high-resolution imagery was used to validate tree and land cover across these gradients. Although the regional forest cover map was comparable in accuracy to a widely-used global forest map (the Global Forest Change of Hansen et al., also known as the GFC), another global forest map (derived from a cropland dataset) had the highest accuracy. Both global and regional forest cover products showed small to severe biases along biophysical gradients. Unlike the regional map, the global GFC map strongly underestimated tree cover (>10% difference) below 189 mm of precipitation and at elevations above 2000 m, with a larger bias for precipitation. All map products misclassified agricultural fields as forest, but the GFC product particularly misclassified row crops and perennial erect crops (banana, oil palm, and coffee), with maximum tree cover in agricultural fields of 89%–100% across all crops. Our analysis calls into further question the utility of the GFC product for global forest monitoring outside humid regions, indicating that, in tropical regions, the GFC product is most accurate in areas with high, aseasonal rainfall, low relief, and low cropland area. Given that forest product errors are spatially distributed along biophysical gradients, researchers should account for these spatial biases when attempting to analyze or generate forest map products.

Simulating precipitation and temperature in the Lake Champlain basin using a regional climate model: limitations and uncertainties

The Lake Champlain Basin has socioeconomic and ecological significance for the Northeastern United States and Quebec, Canada. Temperatures and extreme precipitation events have been increasing across this region over the past three decades. Accurate, high-resolution climate simulations are critical to assessing potential climate change risk in the Lake Champlain Basin. We evaluate the performance of a regional climate model, the Weather Research and Forecasting (WRF) model, to downscale ERA-Interim reanalysis data to 4 km for the Lake Champlain Basin. Specifically, we compare an ensemble of five WRF experiments with different physics configurations using a one-way, triple-nested domain (36, 12, and 4 km) over three 5-year periods (1980–1984, 1995–1999, and 2010–2014) to Daymet, a gridded observational dataset. We find that WRF simulations of the Lake Champlain Basin generally reproduce the observed temperature and precipitation seasonal cycles, but have cold and wet biases. The simulation of mean temperature by WRF is most sensitive to the choice of radiation scheme, while the simulation of mean precipitation is most sensitive to the choice of radiation, cumulus, and microphysics scheme. We find that turning the cumulus scheme on improves the simulation of the precipitation seasonal cycle at a 4 km resolution, but also substantially enhances the wet bias. Using a coarser resolution (36 km) produces smaller regionally averaged precipitation biases, but not improved correlations between simulated and observed monthly precipitation. Both spatial resolution and turning the cumulus scheme off have minor effects on simulated temperature.

Representation of the rain shadow effect in Patagonia using an orographic‐derived regional climate model

AbstractThis study focuses on Patagonia, where Foehn events observed in the lee of the Andes mountains are not yet well simulated by state‐of‐the‐art climate models. It has been agreed that one source of this shortcoming is related to the poor relief representation in models. To resolve this need, a common method used is to enhance the spatial resolution of the model to retrieve a more complex surface elevation, at the expense of calculation time or surface area covered. This paper tackles the problem from a different angle by addressing the Digital Elevation Model (DEM) generalization, that is, the altitudes generalization from a high‐resolution DEM to a coarser resolution grid model. Most current climate models use DEM generalization methods that smooth the relief, a key controlling factor in Foehn events modelling. The aim of this study is to compare three original methods of DEM generalization (percentile 90 [P90], envelope maximum [EM], and thalweg and crests [TC]) and to evaluate their impact on simulated precipitation and temperature fields on the eastern part of Patagonia, where warm and dry air masses are expected. Thanks to MAR, a Regional Climate Model, we validate the models at 10 and 5 km resolutions against the Climate Research Unit and perform three sensitivity experiments involving a change in the DEM generalization. Our results show that (a) a finer spatial resolution can slightly improve the temperature biases, however, it cannot resolve the precipitation biases and (b) a more appropriate use of DEM generalization induces a significant decrease in precipitation for the P90 and EM methods and an increase in mean temperature for all three methods in the study area. This study serves as a recommendation for a better use of DEM generalization in climate models performing in Patagonia, but also regions sharing the same orographic features as the Patagonian relief.

Possible hydrologic forecasting improvements resulting from advancements in precipitation estimation and forecasting for a real-time flood forecast system in the Ohio River Valley, USA

Errors in hydrometeorological forcings for hydrologic modeling lead to considerable prediction uncertainty of hydrologic variables. Analyses of Quantitative Precipitation Estimate (QPE) and Quantitative Precipitation Forecast (QPF) errors over the Ohio River Valley were made to quantify QPE and QPF errors and identify hydrologic impacts of forcing errors and possible improvements resulting from advancements in precipitation estimation and forecasting. Monthly, seasonal, and annual bias analyses of Ohio River Forecast Center (OHRFC) NEXt-generation RADar (NEXRAD) based Stage III and Multisensor Precipitation Estimator (MPE) precipitation estimates, for the period 1997-2016, were made with respect to Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates. Verification of QPF from NWS River Forecast Centers from the NOAA/NWS National Precipitation Verification Unit (NPVU) was compared to QPF verification measures from several numerical weather prediction models and the NOAA/NWS Weather Prediction Center (WPC). Improvements in NEXRAD based QPE over the OHRFC area have been dramatic from 1997 to present. However, from the perspective of meeting hydrologic forecasting needs, QPF shows marginal improvement. A hydrologic simulation experiment illustrates the sensitivity of hydrologic forecasts to QPF errors indicated by QPF Threat Score (TS) values. A monte carlo experiment shows there can be considerable hydrologic forecast error associated with QPF at expected WPC TS levels and, importantly, that higher TS values do not necessarily translate into improved hydrologic forecasts. These results have significant implications for real-time hydrologic forecasting. First, experimental results demonstrate the value gained in terms of improvements in hydrologic modeling accuracy from long-term radar-based precipitation bias reductions. Second, experimental results show that improvements made in terms of QPF have marginal effect on hydrologic prediction and that, even with significant improvement in QPF accuracy, we should expect large hydrologic prediction uncertainty. These issues are discussed more fully as they relate to the future of operational hydrology and ensemble hydrologic forecasting.

Near-Surface Biases in ERA5 Over the Canadian Prairies

We quantify the biases in the diurnal cycle of air temperature in ERA5, using hourly climate station data for four stations in Saskatchewan, Canada. Compared with ERA-Interim, the biases in ERA5 have been greatly reduced, and show no differences with snow cover. We compute fits to the ERA5 mean air temperature biases based on ERA5 effective cloud albedo. They can be used to improve the ERA5 diurnal cycle of air temperature for modeling agricultural processes. Diurnally, ERA5 has a negative wind speed bias, which increases quasi-linearly with wind speed, and is greater in the daytime than at night. We evaluate ERA5 precipitation against the original climate station precipitation data, and a second generation adjusted precipitation dataset by Mekis and Vincent [2011]. For the warm season, ERA5 has a high bias of 8±9% above the Mekis dataset. ERA5 is -22±7% below the Mekis estimate in winter, suggesting that their correction with snow may be too large. It is likely that the ERA5 precipitation bias is small, which is encouraging for agricultural modelling. Data from a BSRN site near Regina shows that the biases in the downwelling shortwave and longwave radiation estimates in ERA5 are small, and have changed little from ERA-Interim. We showed that the annual cycle of the Saskatchewan surface energy and water budgets in ERA5 are realistic. In particular the damping of extremes in summer precipitation by the extraction of soil water is comparable in ERA5 to our earlier observational estimate based on gravity satellite data.

Climatic trends and regional climate models intercomparison over the CORDEX‐CAM (Central America, Caribbean, and Mexico) domain

AbstractAn intercomparison of three regional climate models (RCMs) (PRECIS‐HadRM3P, RCA4, and RegCM4) was performed over the Coordinated Regional Dynamical Experiment (CORDEX)—Central America, Caribbean, and Mexico (CAM) domain to determine their ability to reproduce observed temperature and precipitation trends during 1980–2010. Particular emphasis was given to the North American monsoon (NAM) and the mid‐summer drought (MSD) regions. The three RCMs show negative (positive) temperature (precipitation) biases over the mountains, where observations have more problems due to poor data coverage. Observations from the Climate Research Unit (CRU) and ERA‐Interim show a generalized warming over the domain. The most significant warming trend (≥0.34°C/decade) is observed in the NAM, which is moderately captured by the three RCMs, but with less intensity; each decade from 1970 to 2016 has become warmer than the previous ones, especially during the summer (mean and extremes); this warming appears partially related to the positive Atlantic Multidecadal Oscillation (+AMO). CRU, GPCP, and CHIRPS show significant decreases of precipitation (less than −15%/decade) in parts of the southwest United States and northwestern Mexico, including the NAM, and a positive trend (5–10%/decade) in June–September in eastern Mexico, the MSD region, and northern South America, but longer trends (1950–2017) are not statistically significant. RCMs are able to moderately simulate some of the recent trends, especially in winter. In spite of their mean biases, the RCMs are able to adequately simulate inter‐annual and seasonal variations. Wet (warm) periods in regions affected by the MSD are significantly correlated with the +AMO and La Niña events (+AMO and El Niño). Summer precipitation trends from GPCP show opposite signs to those of CRU and CHIRPS over the Mexican coasts of the southern Gulf of Mexico, the Yucatan Peninsula, and Cuba, possibly due to data limitations and differences in grid resolutions.

Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes

Abstract Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

The Environment and Climate Change Canada solid precipitation intercomparison data from Bratt's Lake and Caribou Creek, Saskatchewan

Abstract. Prior to the beginning of the World Meteorological Organization's (WMO) Solid Precipitation Inter-Comparison Experiment (SPICE, 2013–2015), two precipitation measurement intercomparison sites were established in Saskatchewan to help assess the systematic bias in the automated gauge measurement of solid precipitation and the impact of wind on the undercatch of snow. Caribou Creek, located in the southern boreal forest, and Bratt's Lake, located in the southern plains, are a contribution to the international SPICE project but also to examine national and regional issues in measuring solid precipitation, including regional assessment of wind bias in precipitation gauges and windshield configurations commonly used in Canadian monitoring networks. Overlapping with WMO-SPICE, the Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP) occurred from 2014 to 2015, involving other enhanced observations and cold regions research projects in the same geographical domain as the Saskatchewan SPICE sites. Following SPICE, the two Saskatchewan sites continued to collect core meteorological data (temperature, humidity, wind speed, etc.) as well as precipitation observations via several automated gauge configurations, including the WMO automated reference and the Meteorological Service of Canada's (MSC) network gauges. In addition, manual snow surveys to collect snow cover depth, density, and water equivalent were completed over the duration of the winter periods at the northern Caribou Creek site. Starting in the fall of 2013, the core intercomparison precipitation and ancillary data continued to be collected through the winter of 2017. Automated observations were obtained at a temporal resolution of 1 min, subjected to a rigorous quality control process, and aggregated to a resolution of 30 min. The manual snow surveys at Caribou Creek were typically performed every second week during the SPICE field program with monthly surveys following the end of the SPICE intercomparison period. The Saskatchewan SPICE data are available at https://doi.org/10.18164/63773b5b-5529-4b1e-9150-10acb84d59f0 (Smith and Yang, 2018). The data collected at the Saskatchewan SPICE sites will continue to be useful for transfer function testing, numerical weather prediction and hydrological forecasting verification, ground truth for remote-sensing applications, as well as providing reference precipitation measurements for other concurrent research applications in the cold regions.

Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.