scholarly journals The Predictability of Soil Moisture and Near-Surface Temperature in Hindcasts of the NCEP Seasonal Forecast Model

2003 ◽  
Vol 16 (3) ◽  
pp. 510-521 ◽  
Author(s):  
Masao Kanamitsu ◽  
Cheng-Hsuan Lu ◽  
Jae Schemm ◽  
Wesley Ebisuzaki
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Near Surface

scholarly journals A Comparison of the Noah and OSU Land Surface Models in the ECPC Seasonal Forecast Model

2007 ◽  
Vol 8 (5) ◽  
pp. 1031-1048 ◽  
Author(s):  
Laurel L. De Haan ◽  
Masao Kanamitsu ◽  
Cheng-Hsuan Lu ◽  
John O. Roads
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Forecast Model ◽  
Atmospheric Model ◽  
Seasonal Forecast ◽  
Surface Model ◽  
Near Surface ◽  
Temperature Bias ◽  
Congo River Basin

Abstract The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Center’s (ECPC’s) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (OSU) LSM using two sets of 10-member 50-yr Atmospheric Model Intercomparison Project (AMIP) runs. The climatological biases of several fields tend to increase with the Noah LSM. The differences in near-surface temperature bias are traced to changes in the energy budget. In addition to climatology, the variability and skill (anomaly correlation with observations) of the two ensembles are considered. Unlike the climatology, the near-surface temperature skill of the ECPC SFM generally improves with the Noah LSM. Other climatological fields, such as precipitation, show little change in skill. While the global results are mixed, there are however significant regional improvements over Africa both in terms of climatological bias and skill. In the central African Congo River basin, the Noah LSM removed a warm-dry bias and improved upon the near-surface temperature skill of the OSU LSM. In the African Sahel, the Noah LSM greatly enhanced the climatology, variability, and skill of the ECPC SFM as well as improving the location of the African easterly jet.

scholarly journals Increase in Near-Surface Temperature Simulation Skill due to Predictive Soil Moisture in a Numerical Seasonal Simulation under Observed SST Forcing

2008 ◽  
Vol 9 (1) ◽  
pp. 48-60 ◽  
Author(s):  
Laurel L. De Haan ◽  
Masao Kanamitsu
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Time Lag ◽  
Forecast Model ◽  
Potential Predictability ◽  
Near Surface ◽  
Anomaly Correlation ◽  
High Soil Moisture ◽  
Sst Forcing ◽  
Northern Brazil

Abstract Two sets of 12-yr eight-member ensemble integrations were run with the Experimental Climate Prediction Center’s (ECPC) seasonal forecast model (SFM) to investigate the sensitivity of near-surface temperature skill to evolving soil moisture. The first ensemble had evolving soil moisture, which was fully interactive with the atmospheric component of the model. The second ensemble had soil moisture fixed to the monthly climatological value. Several regions showed an increase in skill in the evolving soil moisture ensemble, including northeastern Australia, southeastern Africa, Europe, northern Brazil, Western Australia, northwestern Russia, Argentina, western Canada, and Indo-China. A survey of these regions showed that most had sensitivity to soil moisture following a peak rainy season, and of those, most also had a high soil moisture time-lag correlation (soil moisture memory) at that time. In a few of the regions high year-to-year soil moisture variability was an additional potential source of soil moisture sensitivity. The sensitivity to soil moisture is considered both in terms of actual predictability (anomaly correlation with observations) and theoretical potential predictability. It was found that the regions listed above, with evolving soil moisture, have anomaly correlations that are close to the potential predictability, suggesting that the model estimates near-surface temperature in these regions as well as could be expected. However, it was also found that when comparing the two sets of integrations, improvements in potential predictability of one ensemble over the other did not necessarily give a reasonable estimate to improvements in anomaly correlation.

scholarly journals Influence of cracking clays on satellite estimated and model simulated soil moisture

2010 ◽  
Vol 14 (6) ◽  
pp. 979-990 ◽  
Author(s):  
Y. Y. Liu ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
R. A. M. de Jeu ◽  
A. I. J. M. van Dijk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Soil Properties ◽  
Surface Soil ◽  
Clay Soils ◽  
Soil Types ◽  
Soil Porosity ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Community Land Model

Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

Assessing reasons for skilful predictions of winter windstorms over the Atlantic/European region

2020 ◽  
Author(s):  
Lisa Degenhardt ◽  
Gregor Leckebusch ◽  
Adam Scaife
Keyword(s):  
Large Scale ◽  
Surface Wind ◽  
Forecast Model ◽  
Surface Wind Speed ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Financial Industry ◽  
Near Surface ◽  
Skill Scores

<p>Severe Atlantic winter storms are affecting densely populated regions of Europe (e.g. UK, France, Germany, etc.). Consequently, different parts of the society, financial industry (e.g., insurance) and last but not least the general public are interested in skilful forecasts for the upcoming storm season (usually December to March). To allow for a best possible use of steadily improved seasonal forecasts, the understanding which factors contribute to realise forecast skill is essential and will allow for an assessment whether to expect a forecast to be skilful or not.</p><p>This study analyses the predictability of the seasonal forecast model of the UK MetOffice, the GloSea5. Windstorm events are identified and tracked following Leckebusch et al. (2008) via the exceedance of the 98<sup>th</sup> percentile of the near surface wind speed.</p><p>Seasonal predictability of windstorm frequency in comparison to observations (based e.g., on ERA5 reanalysis) are calculated and different statistical methods (skill scores) are compared.</p><p>Large scale patterns (e.g., NAO, AO, EAWR, etc.) and dynamical factors (e.g., Eady Growth Rate) are analysed and their predictability is assessed in comparison to storm frequency forecast skill. This will lead to an idea how the forecast skill of windstorms is depending on the forecast skill of forcing factors conditional to the phase of large-scale variability modes. Thus, we deduce information, which factors are most important to generate seasonal forecast skill for severe extra-tropical windstorms.</p><p>The results can be used to get a better understanding of the resulting skill for the upcoming windstorm season.</p>

scholarly journals Sensitivity of Numerical Weather Forecasts to Initial Soil Moisture Variations in CFSv2

2016 ◽  
Vol 31 (6) ◽  
pp. 1973-1983 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Subhadeep Halder
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Time Scales ◽  
Land Surface ◽  
Forecast Model ◽  
Surface Fluxes ◽  
Near Surface ◽  
Weather Forecasts ◽  
Initial Soil Moisture ◽  
Initial Soil

Abstract When initial soil moisture is perturbed among ensemble members in the operational NWS global forecast model, surface latent and sensible fluxes are immediately affected much more strongly, systematically, and over a greater area than conventional land–atmosphere coupling metrics suggest. Flux perturbations are likewise transmitted to the atmospheric boundary layer more formidably than climatology-based metrics would indicate. Impacts are not limited to the traditional land–atmosphere coupling hot spots, but extend over nearly all ice-free land areas of the globe. Key to isolating this effect is that initial atmospheric states are identical among quantities correlated, pinpointing soil moisture and snow cover. A consequence of this high sensitivity is that significant positive impacts of realistic land surface initialization on the skill of deterministic near-surface temperature and humidity forecasts are also immediate and nearly universal during boreal spring and summer (the period investigated) and persist for at least 3 days over most land areas. Land surface initialization may be more broadly important for weather forecasts than previously realized, as the research focus historically has been on subseasonal-to-seasonal time scales. This study attempts to bridge the gap between climate studies with their associated coupling assessments and weather forecast time scales. Furthermore, errors in land surface initialization and shortcomings in the parameterization of atmospheric processes sensitive to surface fluxes may have greater consequences than previously recognized, the latter exemplified by the lack of impact on precipitation forecasts even though the simulation of boundary layer development is shown to be greatly improved with realistic soil moisture initialization.

scholarly journals Impacts of Irrigation on Summertime Temperatures in the Pacific Northwest

2020 ◽  
Vol 24 (1) ◽  
pp. 1-26
Author(s):  
Patricia M. Lawston ◽  
Joseph A. Santanello ◽  
Brian Hanson ◽  
Kristi Arsensault
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Signal ◽  
Near Surface ◽  
Surface Temperatures ◽  
Land Surface Temperatures

AbstractIrrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.

scholarly journals Effect of Realistic Soil Moisture Initialization on the Canadian CanCM3 Seasonal Forecast Model

2012 ◽  
Vol 50 (4) ◽  
pp. 466-474 ◽  
Author(s):  
Gordon Drewitt ◽  
Aaron A. Berg ◽  
William J. Merryfield ◽  
Woo-Sung Lee
Keyword(s):  
Soil Moisture ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Soil Moisture Initialization

scholarly journals Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

2014 ◽  
Vol 27 (21) ◽  
pp. 7976-7993 ◽  
Author(s):  
Alexis Berg ◽  
Benjamin R. Lintner ◽  
Kirsten L. Findell ◽  
Sergey Malyshev ◽  
Paul C. Loikith ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temperature Distribution ◽  
Surface Temperature ◽  
Extreme Temperature ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Near Surface ◽  
Soil Moisture Dynamics ◽  
Available Energy ◽  
Mean And Variance ◽  
Moisture Dynamics

Abstract Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional “hotspots” of land–atmosphere coupling. Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model.

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