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Randal D. Koster, Anthony M. DeAngelis, Siegfried D. Schubert, and Andrea M. Molod

Abstract

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

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Hamish McGowan, Kara Borthwick, Andrew Schwartz, John Nik Callow, Shane Bilish, and Stuart Browning

Abstract

Atmospheric rivers (ARs) are tropospheric corridors that provide ~90% of poleward water vapor transport. They are predicted to increase in frequency and intensity if global warming continues unabated. Here we present a case study of the first direct observations of the impact of AR rain-on-snow (RoS) events on the marginal snowpack of the Australian Alps. Reanalysis data show ARs embedded within strong northwesterly airflow extended over 4000 km from the eastern Indian Ocean to southeast Australia, where orographic processes enhanced RoS. We quantify for the first-time radiation and turbulent energy flux exchanges using eddy covariance and the contribution of rain heat flux to the snowpack during the AR RoS events. The hydrological response of an above snow line catchment that includes Australia’s highest peak during the events was rapid, with discharge increasing by nearly two orders of magnitude above historical mean winter discharge. This reflects the isothermal properties of the marginal Australian snowpack, where small increases in energy from RoS can trigger rapid snowmelt leading to flash flooding. Discharge decreased quickly following the passage of the ARs and onset of cold air advection. Based on climate projections of ≈+2.5°C warming in the Australian Alps by midcentury combined with an already historically, close-to-ripe snowpack, we postulate that AR induced RoS events will accelerate the loss of snow cover.

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Guofeng Zhu, Zhuanxia Zhang, Huiwen Guo, Yu Zhang, Leilei Yong, Qiaozhuo Wan, Zhigang Sun, and Huiying Ma

Abstract

As raindrops fall from the cloud base to the ground, evaporation below those clouds affects the rain’s isotope ratio, reduces precipitation in arid areas, and impacts the local climate. Therefore, in arid areas with scarce water resources and fragile ecological environments, the below-cloud evaporation is an issue of great concern. Based on 406 event-based precipitation samples collected from nine stations in the Shiyang River basin (SRB) in the northwest arid area, global meteorological water line (GMWL) and local meteorological water line (LMWL) are compared, and the Stewart model is used to study the effect of spatial and temporal variation of below-cloud evaporation on isotope values in different geomorphic units at the SRB. Furthermore, factors influencing below-cloud evaporation are analyzed. The results show that 1) the change of d-excess (Δd) in precipitation at the SRB and the residual ratio of raindrop evaporation (f) vary in time and space. With regard to temporal variation, the intensity of below-cloud evaporation is described by summer < autumn < winter < spring. Regarding spatial variation, the below-cloud evaporation in mountain areas is weaker than in oases and deserts. The intensity of below-cloud evaporation in mountain areas increases with decreasing altitude, and the below-cloud evaporation in oasis and desert areas is affected by local climatic conditions. 2) Below-cloud evaporation is also affected by local transpiration evaporation, especially around reservoirs. Reservoirs increase the relative humidity of the air nearby, weakening below-cloud evaporation. This study deepens our understanding of the water cycle process in arid areas.

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Xin Li, Guoyu Ren, Qinglong You, Suyan Wang, and Wen Zhang

Abstract

Soil moisture is an important variable of the climate system and is used to measure dry–wet change in hydro-climate. The warming trend has slowed in China over the past 20 years since 1998, and how the soil moisture changes in this period deserves our attention. With North China as a research region, this study uses the Global Land Data Assimilation System and ground observations to investigate the causes of changes in soil moisture during 1998–2017 versus 1961–1997. The results show that: (1) annual mean soil moisture experienced an almost continued decrease from to 1960s to 2010s, and no pause in the decrease of soil moisture over the regional warming slowdown of the past 20 years could be detected; (2) with the stabilization or even increase in solar radiation and wind speed as well as the continuous increase land surface air temperature, the impact of potential evapotranspiration on soil moisture gradually became prominent, and the impact of precipitation decreased, since 1998; (3) the percent contribution of annual potential evapotranspiration to soil moisture variation increased by 26% during 1998–2017 relative to that in 1961–1997, and the percent contribution of summer potential evapotranspiration even increased by 45%. Our results will provide insight into the land surface water budget and mechanism involved in drought development in North China.

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J. F. González-Rouco, N. J. Steinert, E. García-Bustamante, S. Hagemann, P. de Vrese, J. H. Jungclaus, S. J. Lorenz, C. Melo-Aguilar, F. García-Pereira, and J. Navarro

Abstract

The representation of the thermal and hydrological states in Land Surface Models is important for a realistic simulation of land-atmosphere coupling processes. The available evidence indicates that the simulation of subsurface thermodynamics in Earth System Models is inaccurate due to a zero-heat-flux bottom boundary condition being imposed too close to the surface. In order to assess the influence of soil model depth on the simulated terrestrial energy and subsurface thermal state, sensitivity experiments have been carried out in piControl, historical and RCP scenarios. A deeper bottom boundary condition placement has been introduced into the JSBACH land surface model by enlarging the vertical stratification from 5 to 12 layers, thereby expanding its depth from 9.83 to 1416.84 m. The model takes several hundred years to reach an equilibrium state in stand-alone piControl simulations. A depth of 100 m is necessary, and 300 m recommendable, to handle the warming trends in historical and scenario simulations. Using a deep bottom boundary, warming of the soil column is reduced by 0.5 to 1.5 K in scenario simulations over most land areas, with the largest changes occurring in northern high latitudes, consistent with polar amplification. Energy storage is 3 to 5 times larger in the deep than in the shallow model and increases progressively with additional soil layers until the model depth reaches about 200 m. While the contents of Part I focus on the sensitivity of subsurface thermodynamics to enlarging the space for energy, Part II (Steinert et al. 2021) addresses the sensitivity to changing the space for water and improving hydrological and phase-change interactions.

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N. J. Steinert, J. F. González-Rouco, P. de Vrese, E. García-Bustamante, S. Hagemann, C. Melo-Aguilar, J. H. Jungclaus, and S. J. Lorenz

Abstract

The impact of various modifications of the JSBACH Land Surface Model to represent soil temperature and cold-region hydro-thermodynamic processes in climate projections of the 21st century is examined. We explore the sensitivity of JSBACH to changes in the soil thermodynamics, energy balance and storage, and the effect of including freezing and thawing processes. The changes involve 1) the net effect of an improved soil physical representation and 2) the sensitivity of our results to changed soil parameter values and their contribution to the simulation of soil temperatures and soil moisture, both aspects being presented in the frame of an increased bottom boundary depth from 9.83 m to 1418.84 m. The implementation of water phase changes and supercooled water in the ground creates a coupling between the soil thermal and hydrological regimes through latent heat exchange. Momentous effects on subsurface temperature of up to ±3 K, together with soil drying in the high northern latitudes, can be found at regional scales when applying improved hydro-thermodynamic soil physics. The sensitivity of the model to different soil parameter datasets occurs to be low but shows important implications for the root zone soil moisture content. The evolution of permafrost under pre-industrial forcing conditions emerges in simulated trajectories of stable states that differ by 4 – 6 • 106 km2 and shows large differences in the spatial extent of 105 –106 km2 by 2100, depending on the model configuration.

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David J. Lorenz, Jason A. Otkin, Benjamin Zaitchik, Christopher Hain, and Martha C. Anderson

Abstract

Probabilistic forecasts of changes in soil moisture and an Evaporative Stress Index (ESI) on sub-seasonal time scales over the contiguous U.S. are developed. The forecasts use the current land surface conditions and numerical weather prediction forecasts from the Sub-seasonal to Seasonal (S2S) Prediction Project. Changes in soil moisture are quite predictable 8-14 days in advance with 50% or more of the variance explained over the majority of the contiguous U.S.; however, changes in ESI are significantly less predictable. A simple red noise model of predictability shows that the spatial variations in forecast skill are primarily a result of variations in the autocorrelation, or persistence, of the predicted variable, especially for the ESI. The difference in overall skill between soil moisture and ESI, on the other hand, is due to the greater soil moisture predictability by the numerical model forecasts. As the forecast lead time increases from 8-14 days to 15-28 days, however, the autocorrelation dominates the soil moisture and ESI differences as well. An analysis of modelled transpiration, and bare soil and canopy water evaporation contributions to total evaporation, suggests improvements to the ESI forecasts can be achieved by estimating the relative contributions of these components to the initial ESI state. The importance of probabilistic forecasts for reproducing the correct probability of anomaly intensification is also shown.

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Pin-Lun Li, Chia-Jeng Chen, and Liao-Fan Lin

Abstract

Satellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multi-satellitE Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation_near-real-time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulation: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.

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Benjamin I Cook, Kimberly Slinski, Christa Peters-Lidard, Amy McNally, Kristi Arsenault, and Abheera Hazra

Abstract

Terrestrial water storage (TWS) provides important information on terrestrial hydroclimate and may have value for seasonal forecasting because of its strong persistence. We use the NASA Hydrological Forecast and Analysis System (NHyFAS) to investigate TWS forecast skill over Africa and assess its value for predicting vegetation activity from satellite estimates of leaf area index (LAI). Forecast skill is high over East and Southern Africa, extending up to 3–6 months in some cases, with more modest skill over West Africa. Highest skill generally occurs during the dry season or beginning of the wet season when TWS anomalies from the previous wet season are most likely to carry forward in time. In East Africa, this occurs prior to and during the transition into the spring “Long Rains” from January–March, while in Southern Africa this period of highest skill starts at the beginning of the dry season in April and extends through to the start of the wet season in October. TWS is highly and positively correlated with LAI, and a logistic regression model shows high cross-validation skill in predicting above or below normal LAI using TWS. Combining the LAI regression model with the NHyFAS forecasts, 1-month lead LAI predictions have high accuracy over East and Southern Africa, with reduced but significant skill at 3-month leads over smaller sub-regions. This highlights the potential value of TWS as an additional source of information for seasonal forecasts over Africa, with direct applications to some of the most vulnerable agricultural regions on the continent.

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Daniel Regenass, Linda Schlemmer, Oliver Fuhrer, Jean-Marie Bettems, Marco Arpagaus, and Christoph Schär

Abstract

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land-surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land-surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions ofTERRAML, the land-surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 km2 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

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