scholarly journals Effects of realistic land surface initializations on subseasonal to seasonal soil moisture and temperature predictability in North America and in changing climate simulated by CCSM4

2014 ◽  
Vol 119 (23) ◽  
pp. 13,250-13,270 ◽  
Author(s):  
Sanjiv Kumar ◽  
Paul A. Dirmeyer ◽  
David M. Lawrence ◽  
Timothy DelSole ◽  
Eric L. Altshuler ◽  
...  
2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  

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.


2019 ◽  
Vol 20 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting

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.


2017 ◽  
Author(s):  
Carmelo Cammalleri ◽  
Jürgen V. Vogt ◽  
Bernard Bisselink ◽  
Ad de Roo

Abstract. Agricultural drought events can affect large regions across the World, implying the urge for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/) the suitability of modelled and/or satellite-derived proxy of soil moisture anomalies was investigated. In this study, three datasets have been evaluated as possible proxies of root zone soil moisture anomalies: (1) soil moisture from the Lisflood distributed hydrological model (LIS), (2) remotely sensed land surface temperature data from the MODIS satellite (LST), and (3) the combined passive/active microwave skin soil moisture dataset developed by ESA (CCI). Due to the independency of these three datasets, the Triple Collocation (TC) technique has been applied, aiming at quantifying the likely error associated to each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, Southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as assessment of the accuracy of each method. A clear outcome of the TC analysis is the good performance of remote sensing datasets, especially CCI, over dry regions such as Australia and Southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, these results can be used to design an ensemble system that exploits the advantages of each dataset.


2009 ◽  
Vol 10 (6) ◽  
pp. 1355-1378 ◽  
Author(s):  
Christopher L. Castro ◽  
Adriana B. Beltrán-Przekurat ◽  
Roger A. Pielke

Abstract Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.


2017 ◽  
Vol 21 (12) ◽  
pp. 6329-6343 ◽  
Author(s):  
Carmelo Cammalleri ◽  
Jürgen V. Vogt ◽  
Bernard Bisselink ◽  
Ad de Roo

Abstract. Agricultural drought events can affect large regions across the world, implying the need for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/), the suitability of three datasets as possible representations of root zone soil moisture anomalies has been evaluated: (1) the soil moisture from the Lisflood distributed hydrological model (namely LIS), (2) the remotely sensed Land Surface Temperature data from the MODIS satellite (namely LST), and (3) the ESA Climate Change Initiative combined passive/active microwave skin soil moisture dataset (namely CCI). Due to the independency of these three datasets, the triple collocation (TC) technique has been applied, aiming at quantifying the likely error associated with each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as an assessment of the accuracy of each method. Even if no definitive statement on the spatial distribution of errors can be provided, a clear outcome of the TC analysis is the good performance of the remote sensing datasets, especially CCI, over dry regions such as Australia and southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, the results of the error analysis are used to design a weighted-average ensemble system that exploits the advantages of each dataset.


2019 ◽  
Vol 32 (14) ◽  
pp. 4525-4545 ◽  
Author(s):  
Haiyan Teng ◽  
Grant Branstator ◽  
Ahmed B. Tawfik ◽  
Patrick Callaghan

Abstract A series of idealized prescribed soil moisture experiments is performed with the atmosphere/land stand-alone configuration of the Community Earth System Model, version 1, in an effort to find sources of predictability for high-impact stationary wave anomalies observed in recent boreal summers. We arbitrarily prescribe soil water to have a zero value at selected domains in the continental United States and run 100-member ensembles to examine the monthly and seasonal mean response. Contrary to the lack of a substantial response in the boreal winter, the summertime circulation response is robust, consistent, and circumglobal. While the stationary wave response over the North America and North Atlantic sectors can be well explained by the reaction of a linear dynamical system to heating anomalies caused by the imposed dry land surface, nonlinear processes involving synoptic eddies play a crucial role in forming the remote response in Eurasia and the North Pacific Ocean. A number of other possible factors contributing to the circulation responses are also discussed. Overall, the experiments suggest that, in the boreal summer, soil moisture may contribute to the predictability of high-impact stationary wave events, which can impact regions that are great distances from these source regions.


2019 ◽  
Vol 32 (10) ◽  
pp. 2707-2734 ◽  
Author(s):  
Sanjiv Kumar ◽  
Matthew Newman ◽  
Yan Wang ◽  
Ben Livneh

Abstract Soil moisture anomalies within the root zone (roughly, soil depths down to ~0.4 m) typically persist only a few months. Consequently, land surface–related climate predictability research has often focused on subseasonal to seasonal time scales. However, in this study of multidecadal in situ datasets and land data assimilation products, we find that root zone soil moisture anomalies can recur several or more seasons after they were initiated, indicating potential interannual predictability. Lead–lag correlations show that this recurrence often happens during one fixed season and also seems related to the greater memory of soil moisture anomalies within the layer beneath the root zone, with memory on the order of several months to over a year. That is, in some seasons, notably spring and summer when the vertical soil water potential gradient reverses sign throughout much of North America, deeper soil moisture anomalies appear to return to the surface, thereby restoring an earlier root zone anomaly that had decayed. We call this process “reemergence,” in analogy with a similar seasonally varying process (with different underlying physics) providing winter-to-winter memory to the extratropical ocean surface layer. Pronounced spatial and seasonal dependence of soil moisture reemergence is found that is frequently, but not always, robust across datasets. Also, some of its aspects appear sensitive to spatial and temporal sampling, especially within the shorter available in situ datasets, and to precipitation variability. Like its namesake, soil moisture reemergence may enhance interannual-to-decadal variability, notably of droughts. Its detailed physics and role within the climate system, however, remain to be understood.


2012 ◽  
Vol 25 (13) ◽  
pp. 4744-4749 ◽  
Author(s):  
Zhichang Guo ◽  
Paul A. Dirmeyer ◽  
Timothy DelSole ◽  
Randal D. Koster

Abstract Total predictability within a chaotic system like the earth’s climate cannot increase over time. However, it can be transferred between subsystems. Predictability of air temperature and precipitation in numerical model forecasts over North America rebounds during late spring to summer because of information stored in the land surface. Specifically, soil moisture anomalies can persist over several months, but this memory cannot affect the atmosphere during early spring because of a lack of coupling between land and atmosphere. Coupling becomes established in late spring, enabling the effects of soil moisture anomalies to increase atmospheric predictability in 2-month forecasts begun as early as 1 May. This predictability is maintained through summer and then drops as coupling fades again in fall. This finding suggests summer forecasts of rainfall and air temperature over parts of North America could be significantly improved with soil moisture observations during spring.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Musa Esit ◽  
Sanjiv Kumar ◽  
Ashutosh Pandey ◽  
David M. Lawrence ◽  
Imtiaz Rangwala ◽  
...  

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.


2012 ◽  
Vol 14 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Longfei BING ◽  
Hongbo SU ◽  
Quanqin SHAO ◽  
Jiyuan LIU
Keyword(s):  

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