scholarly journals Seasonal to multi-year soil moisture drought forecasting

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Musa Esit ◽  
Sanjiv Kumar ◽  
Ashutosh Pandey ◽  
David M. Lawrence ◽  
Imtiaz Rangwala ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
System Modeling ◽  
Precipitation Variability ◽  
Surface Processes ◽  
Earth System ◽  
Drought Forecasting ◽  
Earth System Modeling ◽  
Order Of Magnitude

AbstractSoil moisture predictability on seasonal to decadal (S2D) continuum timescales over North America is examined from the Community Earth System Modeling (CESM) experiments. The effects of ocean and land initializations are disentangled using two large ensemble datasets—initialized and uninitialized experiments from the CESM. We find that soil moisture has significant predictability on S2D timescales despite limited predictability in precipitation. On sub-seasonal to seasonal timescales, precipitation variability is an order of magnitude greater than soil moisture, suggesting land surface processes, including soil moisture memory, reemergence, land–atmosphere interactions, transform a less predictable precipitation signal into a more predictable soil moisture signal.

Coupled earth system modeling on heterogeneous HPC architectures with ParFlow in the Terrestrial Systems Modeling Platform

2021 ◽  
Author(s):  
Jaro Hokkanen ◽  
Stefan Kollet ◽  
Jiri Kraus ◽  
Andreas Herten ◽  
Markus Hrywniak ◽  
...  
Keyword(s):  
Land Surface ◽  
Memory Management ◽  
High Performance ◽  
Data Exchange ◽  
System Modeling ◽  
Hydrologic Model ◽  
Earth System ◽  
Single Node ◽  
Earth System Modeling ◽  
Domain Specific

<p>Rapidly changing heterogeneous supercomputer architectures pose a great challenge to many scientific communities trying to leverage the latest technology in high-performance computing. Implementations that simultaneously result in a good performance and developer productivity while keeping the codebase adaptable and well maintainable in the long-term are of high importance. ParFlow, a widely used hydrologic model, achieves these attributes by hiding the architecture-dependent code in preprocessor macros (ParFlow embedded Domain Specific Language, eDSL) and leveraging NVIDIA's Unified Memory technology for memory management. The implementation results in very good weak scaling with up to 26x speedup when using four NVIDIA A100 GPUs per node compared to using the available 48 CPU cores. Good weak scaling is observed using hundreds of nodes on the new JUWELS Booster system at the Jülich Supercomputing Centre, Germany. Furthermore, it is possible to couple ParFlow with other earth system compartment models such as land surface and atmospheric models using the OASIS-MCT coupler library, which handles the data exchange between the different models. The ParFlow GPU implementation is fully compatible with the coupled implementation with little changes to the source code. Moreover, coupled simulations offer interesting load-balancing opportunities for optimal usage of the existing resources. For example, running ParFlow on GPU nodes, and another application component on CPU-only nodes, or efficiently distributing the CPU and GPU resources of a single node between the different application components may result in the best usage of heterogeneous architectures.</p>

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals Mechanisms of Seasonal Soil Moisture Drought Onset and Termination in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Great Plains ◽  
Land Surface ◽  
Tropical Pacific ◽  
Moisture Change ◽  
Southern Plains ◽  
Southern Great Plains ◽  
Continental Scale ◽  
Over Time

AbstractMechanisms of drought onset and termination are examined across North America with a focus on the southern Plains using data from land surface models and regional and global reanalyses for 1979–2017. Continental-scale analysis of covarying patterns reveals a tight coupling between soil moisture change over time and intervening precipitation anomalies. The southern Great Plains are a geographic center of patterns of hydrologic change. Drying is induced by atmospheric wave trains that span the Pacific and North America and place northerly flow anomalies above the southern Plains. In the southern Plains winter is least likely, and fall most likely, for drought onset and spring is least likely, and fall or summer most likely, for drought termination. Southern Plains soil moisture itself, which integrates precipitation over time, has a clear relationship to tropical Pacific sea surface temperature (SST) anomalies with cold conditions favoring dry soils. Soil moisture change, however, though clearly driven by precipitation, has a weaker relation to SSTs and a strong relation to internal atmospheric variability. Little evidence is found of connection of drought onset and termination to driving by temperature anomalies. An analysis of particular drought onsets and terminations on the seasonal time scale reveals commonalities in terms of circulation and moisture transport anomalies over the southern Plains but a variety of ways in which these are connected into the large-scale atmosphere and ocean state. Some onsets are likely to be quite predictable due to forcing by cold tropical Pacific SSTs (e.g., fall 2010). Other onsets and all terminations are likely not predictable in terms of ocean conditions.

scholarly journals A European Network for Earth System Modeling

Eos ◽  
2007 ◽  
Vol 88 (12) ◽  
pp. 143 ◽  
Author(s):  
Sophie Valcke ◽  
Reinhard Budich ◽  
Mick Carter ◽  
Eric Guilyardi ◽  
Marie-Alice Foujols ◽  
...  
Keyword(s):  
System Modeling ◽  
Earth System ◽  
European Network ◽  
Earth System Modeling

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

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