Impact of Snow Representation in Seasonal Forecast Systems

Current seasonal forecast systems have difficulties predicting temperature over continental regions, whereas for some regions with maritime influence their performance is better. The main driver for better skill in maritime regions is related to the ocean and its memory effect. For continental regions, the land surface can become a more important source of predictability on (sub-)seasonal time scales. Besides soil moisture, snow is a crucial component of the land surface as it stores an extensive amount of water and modulates the earth&#8217;s radiation budget each winter season. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture and river water. We compare the snow representation in seasonal forecast systems from four European weather/climate services provided by the Copernicus Climate Change Service (C3S) and their performance in predicting snow, temperature and precipitation. The goal is to identify the impact of the snow initialisation and snow modelling from the four forecasts systems. The first results show that the predicted anomalies of 2m temperature over continental regions correlate with reanalyses only for the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. While the biases among the forecast systems differ, the correlation skills are similar for the same variable, with precipitation having the lowest correlation skills. Furthermore, we will investigate the causal relationships between snow and 2m temperature with time-lagged correlation or similar methods and will consider the whole ensembles of the hindcasts.

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Snow water scarcity induced by record-breaking warm winter in 2020 in Japan

Scientific Reports ◽

10.1038/s41598-020-75440-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Satoshi Watanabe ◽

Shunji Kotsuki ◽

Shinjiro Kanae ◽

Kenji Tanaka ◽

Atsushi Higuchi

Keyword(s):

Water Resources ◽

High Temperatures ◽

Water Scarcity ◽

Land Surface ◽

Snow Water Equivalent ◽

River Basins ◽

Reanalysis Data ◽

Temperature And Precipitation ◽

Snow Water ◽

The Impact

Abstract This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

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Land-surface feedbacks on temperature and precipitation in CMIP6-LS3MIP projections

10.5194/egusphere-egu21-12175 ◽

2021 ◽

Author(s):

Franco Catalano ◽

Andrea Alessandri ◽

Wilhelm May ◽

Thomas Reerink

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Hydrological Cycle ◽

Climate Change Signal ◽

Moisture Change ◽

Climate Conditions ◽

Temperature And Precipitation ◽

Systematic Biases ◽

The Impact

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) aims at diagnosing systematic biases in the land models of CMIP6 Earth System Models and assessing the role of land-atmosphere feedbacks on climate change. Two components of experiments have been designed: the first is devoted to the assessment of the systematic land biases in offline mode (LMIP) while the second component is dedicated to the analysis of the land feedbacks in coupled mode (LFMIP). Here we focus on the LFMIP experiments. In the LFMIP protocol (van den Hurk et al. 2016), which builds upon the GLACE-CMIP configuration, two sets of climate-sensitivity projections have been carried out in amip mode: in the first set (amip-lfmip-pdLC) the land feedbacks to climate change have been disabled by prescribing the soil-moisture states from a climatology derived from &#8220;present climate conditions&#8221; (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O&#8217;Neill et al., 2016) is prescribed. The two sensitivity simulations span the period 1980-2100 with sea surface temperature and sea-ice conditions prescribed from the first member of historical and ScenarioMIP experiments. Two different scenarios are considered: SSP1-2.6 (f1) and SSP5-8.5 (f2).In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071&#8211;2100) in order to isolate the impact of the soil moisture changes on surface climate change. The (2071-2100) minus (1985-2014) temperature change is positive everywhere over land and the climate change signal of precipitation displays a clear intensification of the hydrological cycle in the Northern Hemisphere. Warming and hydrological cycle intensification are larger in SSP5-8.5 scenario. Results show large differences in the feedbacks between wet, transition and semi-arid climates. In particular, over the regions with negative soil moisture change, the 2m-temperature increases significantly while the cooling signal is not significant over all the regions getting wetter. In agreement with Catalano et al. (2016), the larger effects on precipitation due to soil moisture forcing occur mostly over transition zones between dry and wet climates, where evaporation is highly sensitive to soil moisture. The sensitivity of both 2m-temperature and precipitation to soil moisture change is much stronger in the SSP5-8.5 scenario.

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Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0070.1 ◽

2015 ◽

Vol 16 (2) ◽

pp. 917-931 ◽

Cited By ~ 25

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Youfei Zheng ◽

Jicheng Liu ◽

Li Fang ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Radiation ◽

Radiation Budget ◽

Shortwave Radiation ◽

Surface Model ◽

Spatial Coverage ◽

In Situ Observations ◽

The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

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The Impact of Land Surface and Atmospheric Initialization on Seasonal Forecasts with CCSM

Journal of Climate ◽

10.1175/2011jcli3934.1 ◽

2012 ◽

Vol 25 (3) ◽

pp. 1007-1021 ◽

Cited By ~ 24

Author(s):

Daniel A. Paolino ◽

James L. Kinter ◽

Ben P. Kirtman ◽

Dughong Min ◽

David M. Straus

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Large Scale ◽

Climate Model ◽

Seasonal Forecast ◽

Good Representation ◽

Seasonal Forecasts ◽

Coupled Models ◽

Climate System Model ◽

The Impact

Abstract Series of forecast experiments for two seasons investigate the impact of specifying realistic initial states of the land in conjunction with the observed states of the ocean and atmosphere while using the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0). Since direct soil moisture observations adequate for initialization of the land surface do not exist, this study considers proxy data. The authors are able to successfully initialize all components of the CCSM3.0 and produce a good representation of the mean land surface climate in the first season’s forecast. In comparison with a previous set of forecast experiments that had initialized only the observed ocean state, there is firm evidence that this study produces a better representation of the interannual variability of the soil surface. The representation of soil moisture in the fully initialized seasonal forecasts as measured against the reanalysis is improved, due in part to the ability of the CCSM3.0 to persist large-scale anomalies present in the initial soil state. The improvement in the representation of the land surface, in conjunction with the atmospheric initialization, contributes to a skillful seasonal forecast of surface temperature. There is little evidence of an improved forecast of precipitation over land. Results from this study support the use of the CCSM, originally designed for use as a climate model, as a fully initialized seasonal forecast model. The authors suggest that initialization of the land surface state is crucial for skillful seasonal forecasts made with fully coupled models.

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Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

Journal of Hydrometeorology ◽

10.1175/jhm554.1 ◽

2007 ◽

Vol 8 (1) ◽

pp. 68-87 ◽

Cited By ~ 71

Author(s):

Margaret A. LeMone ◽

Fei Chen ◽

Joseph G. Alfieri ◽

Mukul Tewari ◽

Bart Geerts ◽

...

Keyword(s):

Soil Moisture ◽

Latent Heat ◽

Land Surface ◽

Field Experiments ◽

Time Of Day ◽

Horizontal Distribution ◽

Heat Fluxes ◽

Surface Model ◽

Green Vegetation ◽

The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

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On the sources of global land surface hydrologic predictability

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2781-2013 ◽

2013 ◽

Vol 17 (7) ◽

pp. 2781-2796 ◽

Cited By ~ 68

Author(s):

S. Shukla ◽

J. Sheffield ◽

E. F. Wood ◽

D. P. Lettenmaier

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Snow Water Equivalent ◽

Forecast Skill ◽

Streamflow Prediction ◽

Infiltration Capacity ◽

Forecast Period ◽

Data Set ◽

Model Based ◽

Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-3135-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 3135-3149 ◽

Cited By ~ 23

Author(s):

G. Panegrossi ◽

R. Ferretti ◽

L. Pulvirenti ◽

N. Pierdicca

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Ground Truth ◽

Rain Gauge ◽

Precipitation Forecast ◽

Flood Event ◽

Strong Impact ◽

Moisture Field ◽

The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

Earth System Science Data ◽

10.5194/essd-11-101-2019 ◽

2019 ◽

Vol 11 (1) ◽

pp. 101-110 ◽

Cited By ~ 1

Author(s):

James W. Roche ◽

Robert Rice ◽

Xiande Meng ◽

Daniel R. Cayan ◽

Michael D. Dettinger ◽

...

Keyword(s):

Soil Moisture ◽

Sierra Nevada ◽

Land Surface ◽

Forest Canopy ◽

Snow Water Equivalent ◽

Process Models ◽

Series Data ◽

Climate Data ◽

Data Set ◽

Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

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