scholarly journals Temperature Projections over the Indus River Basin of Pakistan Using Statistical Downscaling

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 195
Author(s):  
Muhammad Saleem Pomee ◽  
Elke Hertig
Keyword(s):  
21St Century ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Winter Season ◽  
Circulation Model ◽  
Indus Basin ◽  
Indus River ◽  
Temperature Changes ◽  
Basin Scale ◽  
Maximum Heating

We assessed maximum (Tmax) and minimum (Tmin) temperatures over Pakistan’s Indus basin during the 21st century using statistical downscaling. A particular focus was given to spatiotemporal heterogeneity, reference and General Circulation Model (GCM) uncertainties, and statistical skills of regression models using an observational profile that could significantly be improved by recent high-altitude observatories. First, we characterized the basin into homogeneous climate regions using K-means clustering. Predictors from ERA-Interim reanalysis were then used to model observed temperatures skillfully and quantify reference and GCM uncertainties. Thermodynamical (dynamical) variables mainly governed reference (GCM) uncertainties. The GCM predictors under RCP4.5 and RCP8.5 scenarios were used as “new” predictors in statistical models to project ensemble temperature changes. Our analysis projected non-uniform warming but could not validate elevation-dependent warming (EDW) at the basin scale. We obtained more significant warming during the westerly-dominated seasons, with maximum heating during the winter season through Tmin changes. The most striking feature is a low-warming monsoon (with the possibility of no change to slight cooling) over the Upper Indus Basin (UIB). Therefore, the likelihood of continuing the anomalous UIB behavior during the primary melt season may not entirely be ruled out at the end of the 21st century under RCP8.5.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Towards a regional ocean forecasting system for the IBI (Iberia-Biscay-Ireland area): developments and improvements within the ECOOP project framework

Ocean Science ◽  
2012 ◽  
Vol 8 (2) ◽  
pp. 143-159 ◽  
Author(s):  
S. Cailleau ◽  
J. Chanut ◽  
J.-M. Lellouche ◽  
B. Levier ◽  
C. Maraldi ◽  
...  
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Vertical Mixing ◽  
Circulation Model ◽  
Storm Surges ◽  
Ocean General Circulation Model ◽  
Tidal Mixing ◽  
Basin Scale ◽  
Forecasting System ◽  
Ocean Forecasting

Abstract. The regional ocean operational system remains a key element in downscaling from large scale (global or basin scale) systems to coastal ones. It enables the transition between systems in which the resolution and the resolved physics are quite different. Indeed, coastal applications need a system to predict local high frequency events (inferior to the day) such as storm surges, while deep sea applications need a system to predict large scale lower frequency ocean features. In the framework of the ECOOP project, a regional system for the Iberia-Biscay-Ireland area has been upgraded from an existing V0 version to a V2. This paper focuses on the improvements from the V1 system, for which the physics are close to a large scale basin system, to the V2 for which the physics are more adapted to shelf and coastal issues. Strong developments such as higher regional physics resolution in the NEMO Ocean General Circulation Model for tides, non linear free surface and adapted vertical mixing schemes among others have been implemented in the V2 version. Thus, regional thermal fronts due to tidal mixing now appear in the latest version solution and are quite well positioned. Moreover, simulation of the stratification in shelf areas is also improved in the V2.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

2020 ◽  
Vol 142 (1-2) ◽  
pp. 29-57
Author(s):  
Muhammad Saleem Pomee ◽  
Moetasim Ashfaq ◽  
Bashir Ahmad ◽  
Elke Hertig
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
Statistical Models ◽  
Regional Scale ◽  
Indus Basin ◽  
Complex Data ◽  
Indus River ◽  
Modeling Framework ◽  
Additional Measure ◽  
Indus River Basin

Abstract Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. We also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

scholarly journals Role of Air–Sea Interaction in the Long Persistence of El Niño–Induced North Indian Ocean Warming*

2009 ◽  
Vol 22 (8) ◽  
pp. 2023-2038 ◽  
Author(s):  
Yan Du ◽  
Shang-Ping Xie ◽  
Gang Huang ◽  
Kaiming Hu
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
General Circulation ◽  
El Nino ◽  
Circulation Model ◽  
Ocean Dynamics ◽  
Northwest Pacific ◽  
Basin Scale ◽  
The Mean ◽  
Second Peak

Abstract El Niño induces a basin-wide increase in tropical Indian Ocean (TIO) sea surface temperature (SST) with a lag of one season. The north IO (NIO), in particular, displays a peculiar double-peak warming with the second peak larger in magnitude and persisting well through the summer. Motivated by recent studies suggesting the importance of the TIO warming for the Northwest Pacific and East Asian summer monsoons, the present study investigates the mechanisms for the second peak of the NIO warming using observations and general circulation models. This analysis reveals that internal air–sea interaction within the TIO is key to sustaining the TIO warming through summer. During El Niño, anticyclonic wind curl anomalies force a downwelling Rossby wave in the south TIO through Walker circulation adjustments, causing a sustained SST warming in the tropical southwest IO (SWIO) where the mean thermocline is shallow. During the spring and early summer following El Niño, this SWIO warming sustains an antisymmetric pattern of atmospheric anomalies with northeasterly (northwesterly) wind anomalies north (south) of the equator. Over the NIO as the mean winds turn into southwesterly in May, the northeasterly anomalies force the second SST peak that persists through summer by reducing the wind speed and surface evaporation. Atmospheric general circulation model experiments show that the antisymmetric atmospheric pattern is a response to the TIO warming, suggestive of their mutual interaction. Thus, ocean dynamics and Rossby waves in particular are important for the warming not only locally in SWIO but also on the basin-scale north of the equator, a result with important implications for climate predictability and prediction.

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