scholarly journals Modulation of summer monsoon sub-seasonal surface air temperature over India by soil moisture-temperature coupling

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 53-66
Author(s):  
M. V. S. RAMARAO ◽  
J. SANJAY ◽  
R. KRISHNAN
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Summer Monsoon ◽  
Climate Models ◽  
Monsoon Season ◽  
Surface Air Temperature ◽  
Monsoon Precipitation ◽  
Summer Monsoon Season ◽  
Weather And Climate ◽  
Dry Soil

The influence of soil moisture on the sub-seasonal warmer surface air temperature anomalies during drier soil conditions associated with break spells in the Indian summer monsoon precipitation is explored using observations.  The multi-model analysis of land surface states and fluxes available from the Second Global Soil Wetness Project (GSWP-2) are found useful in understanding the mechanism for this soil moisture-temperature coupling on sub-seasonal timescales. The analysis uses a soil moisture-temperature coupling diagnostic computed based on linear correlations of daily fields. It is shown that the summer surface air temperature variations are linked to intraseasonal variations of the Indian monsoon precipitation, which control the land-climate coupling by modulating the soil moisture variations. Strong coupling mainly occurs during dry soil states within the summer monsoon season over the transition zones between wet and dry climates of central to north-west India. In contrast, the coupling is weak for constantly wet and energy-limited evaporative regimes over eastern India during the entire summer monsoon season. This observational based analysis provided a better understanding of the linkages between the sub-seasonal dry soil states and warm conditions during the Indian summer monsoon season. A proper representation of these aspects of land-atmosphere interactions in weather and climate models used for sub-seasonal and seasonal monsoon forecasting could be critical for several applications, in particular agriculture. The soil moisture-temperature coupling diagnostic used in this study will be a useful metric for evaluating the performance of weather and climate models.

scholarly journals Soil Moisture–Precipitation Feedback Processes in the Indian Summer Monsoon Season

2012 ◽  
Vol 13 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Shakeel Asharaf ◽  
Andreas Dobler ◽  
Bodo Ahrens
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Land Surface ◽  
Climate Model ◽  
Monsoon Season ◽  
Regional Climate ◽  
Precipitable Water ◽  
Small Scale ◽  
Summer Monsoon Season

Abstract Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.

Influence of Eurasian spring snowmelt on surface air temperature in late spring and early summer

2021 ◽  
pp. 1-45
Author(s):  
Juan Zhou ◽  
Zhiyan Zuo ◽  
Qiong He
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Cloud Cover ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Early Summer ◽  
Late Spring ◽  
Shortwave Radiation ◽  
Sensible Heat ◽  
Spring Snowmelt

AbstractThe effect of Eurasian spring snowmelt on surface air temperature (SAT) in late spring (April–May) and early summer (June–July) and the relevant physical mechanisms during 1981–2016 are investigated. Results show that the first mode of the inter-annual Eurasian spring snowmelt represents an east–west dipole anomaly pattern, with an intense center over Siberia and another moderate center around eastern Europe. The European spring snowmelt shows an insignificant relation to the local SAT, whereas the Siberian spring snowmelt has a significant impact on the SAT in late spring and early summer. More Siberian spring snowmelt contributes to higher SAT in late spring and lower SAT in early summer via different mechanisms. In late spring, increased Siberian spring snowmelt corresponds to less local surface albedo and cloud cover, leading to the surface absorbing more shortwave radiation and thereby higher SAT. The sub-surface and deep soil moisture anomalies generated from Siberian spring snowmelt can persist into early summer. Excessive Siberian spring snowmelt corresponds to positive soil moisture anomalies, contributing to decreased sensible heat and increased cloud cover in early summer. Increased cloud cover leads to the surface receiving less shortwave radiation. Thus, lower SAT appears over Siberia in early summer due to reduced sensible heat and shortwave radiation. However, the simulation of Eurasian spring snowmelt variability and its influences on SAT via the snow hydrological effect is still a challenge for the climate models that participated in the Coupled Model Intercomparison Project phase 6.

scholarly journals Variability of Indian summer monsoon: Relationship with surface air temperature anomalies over northern hemisphere

MAUSAM ◽  
2022 ◽  
Vol 44 (2) ◽  
pp. 191-198
Author(s):  
R. K. VERMA
Keyword(s):  
Northern Hemisphere ◽  
Air Temperature ◽  
Summer Monsoon ◽  
Monsoon Rainfall ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Surface Air Temperature ◽  
Time Lags ◽  
Temperature Anomalies ◽  
Air Temperature Anomalies

Thirty year (1950-79) time series of Monsoon Index (MI) is correlated with the gridded surface air temperature data over northern hemisphere land at various time lags of months (i.e., months preceding concurrent and succeeding to the monsoon season) to identify tele-connections of monsoon with the northern hemisphere surface air temperature anomalies. .   Out of three key regions identified which show statistically significant relationship of monsoon rainfall, two regions are in the higher latitudinal belt of 40oN- 70oN over North America and Eurasia which show positive correlations with temperatures during northern winter particularly during  January and February. The third region is located over northwest India and adjoining Pakistan, where the maximum positive correlation is observed to occur during the pre-li1onsoon months of April and May. These relationships suggest that cooler northern hemisphere during the preceding seasons of winter/spring over certain key regions are generally associated with below normal summer monsoon rainfall over India and vice-versa which could be useful predictors for long-range forecasting of monsoon.   There are two large regions in the northern tropics, namely, Asian and African monsoons whose temperatures reveal strong negative correlations with monsoon rainfall during the seasons concurrent and subsequent to the summer monsoon season. However, persistence of this relationship for longer period of about two seasons after the monsoon, suggests the dominant influence of  ENSO (El. Nino-Southern Oscillation) on tropical climate.  

scholarly journals Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Saran Aadhar ◽  
Shanti Shwarup Mahto
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Crop Production ◽  
Monsoon Rainfall ◽  
Root Zone ◽  
Monsoon Season ◽  
Intraseasonal Variability ◽  
Positive Temperature ◽  
Groundwater Abstraction ◽  
Increased Risk

AbstractFlash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals Variations in O<sub>3</sub>, CO, and CH<sub>4</sub> over the Bay of Bengal during the summer monsoon season: Ship-borne measurements and model simulations

2016 ◽  
Author(s):  
Imran A. Girach ◽  
Narendra Ojha ◽  
Prabha R. Nair ◽  
Andrea Pozzer ◽  
Yogesh K. Tiwari ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Bay Of Bengal ◽  
Monsoon Season ◽  
Surface Ozone ◽  
Ozone Production ◽  
Spatial And Temporal Variability ◽  
Summer Monsoon Season ◽  
Rainfall Events ◽  
Model Simulations ◽  
Mixing Ratios

Abstract. We present ship-borne measurements of surface ozone, carbon monoxide and methane over the Bay of Bengal (BoB), the first time such measurements have been taken during the summer monsoon season, as a part of the Continental Tropical Convergence Zone (CTCZ) experiment during 2009. O3, CO, and CH4 mixing ratios exhibited significant spatial and temporal variability in the ranges of 8–54 nmol mol−1, 50–200 nmol mol−1, and 1.57–2.15 µmol mol−1, with means of 29.7 ± 6.8 nmol mol−1, 96 ± 25 nmol mol−1, and 1.83 ± 0.14 µmol mol−1, respectively. The average mixing ratios of trace gases over northern BoB (O3: 30 ± 7 nmol mol−1, CO: 95 ± 25 nmol mol−1, CH4: 1.86 ± 0.12 µmol mol−1), in airmasses from northern or central India, did not differ much from those over central BoB (O3: 27 ± 5 nmol mol−1, CO: 101 ± 27 nmol mol−1, CH4: 1.72 ± 0.14 µmol mol−1), in airmasses from southern India. Spatial variability is observed to be most significant for CH4. The ship-based observations, in conjunction with backward air trajectories and ground-based measurements over the Indian region, are analyzed to estimate a net ozone production of 1.5–4 nmol mol−1 day−1 in the outflow. Ozone mixing ratios over the BoB showed large reductions (by ~ 20 nmol mol−1) during four rainfall events. Temporal changes in the meteorological parameters, in conjunction with ozone vertical profiles, indicate that these low ozone events are associated with downdrafts of free-tropospheric ozone-poor airmasses. While the observed variations in O3 and CO are successfully reproduced using the Weather Research and Forecasting model with Chemistry (WRF-Chem), this model overestimates mean concentrations by about 20 %, generally overestimating O3 mixing ratios during the rainfall events. Analysis of the chemical tendencies from model simulations for a low-O3 event on August 10, 2009, captured successfully by the model, shows the key role of horizontal advection in rapidly transporting ozone-rich airmasses across the BoB. Our study fills a gap in the availability of trace gas measurements over the BoB, and when combined with data from previous campaigns, reveals large seasonal amplitude (~ 39 and ~ 207 nmol mol−1 for O3 and CO, respectively) over the northern BoB.

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

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