scholarly journals Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

2017 ◽  
Vol 21 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Charles Chisanga ◽  
Elijah Phiri ◽  
Vernon Chinene
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Bias Correction ◽  
Coupled Model ◽  
Model Intercomparison ◽  
Statistical Bias ◽  
Intercomparison Project ◽  
Project Data ◽  
Agriculture And Food ◽  
Statistical Bias Correction

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models

2011 ◽  
Vol 12 (4) ◽  
pp. 556-578 ◽  
Author(s):  
Stefan Hagemann ◽  
Cui Chen ◽  
Jan O. Haerter ◽  
Jens Heinke ◽  
Dieter Gerten ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Coupled Model ◽  
Model Output ◽  
Hydrological Models ◽  
Statistical Bias ◽  
Max Planck ◽  
Climate Model Output ◽  
Statistical Bias Correction

Abstract Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)—were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071–2100 are considered relative to 1961–90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.

scholarly journals Assessing the impacts of climate variability and climate change on biodiversity in Lake Nakuru, Kenya

2018 ◽  
Vol 8 (1) ◽  
pp. 13-24 ◽  
Author(s):  
MBOTE BETH WAMBUI ◽  
ALFRED OPERE ◽  
JOHN M. GITHAIGA ◽  
FREDRICK K. KARANJA
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Surface Area ◽  
Coupled Model ◽  
Baseline Period ◽  
Strong Positive Correlation ◽  
Model Intercomparison ◽  
Lake Nakuru ◽  
Lesser Flamingo ◽  
Intercomparison Project

Wambui MB, Opere A, Githaiga MJ, Karanja FK. 2017. Assessing the impacts of climate variability and climate change on biodiversity in Lake Nakuru, Kenya. Bonorowo Wetlands 1: 13-24. This study evaluates the impacts of the raised water levels and the flooding of Lake Nakuru and its surrounding areas on biodiversity, specifically, the phytoplankton and lesser flamingo communities, due to climate change and climate variability. The study was to review and analyze noticed climatic records from 2000 to 2014. Several methods were used to ascertain the past and current trends of climatic parameters (temperature, rainfall and evaporation), and also the physicochemical characteristics of Lake Nakuru (conductivity, phytoplankton, lesser flamingos and the lake depth). These included time series analysis, and trend analysis, so the Pearson’s correlation analysis was used to show a relationship between the alterations in lake conductivity to alterations in population estimates of the lesser flamingos and the phytoplankton. Data set extracted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (IPCC Fifth Assessment Report (AR5) Atlas subset) models were subjected to time series analysis method where the future climate scenarios of near surface temperature, rainfall and evaporation were plotted for the period 2017 to 2100 (projection) for RCP2.6 and RCP8.5 relative to the baseline period 1971 to 2000 in Lake Nakuru were analysed. The results were used to evaluate the impact of climate change on the lesser flamingos and phytoplankton abundance. It was noticed that there was a raise in the mean annual rainfall during the study period (2009 to 2014) which brought the increment in the lake’s surface area from a low area of 31.8 km² in January 2010 to a high of 54.7 km² in Sept 2013, indicating an increment of 22.9 km² (71.92% surface area increment). Mean conductivity of the lake also lessened leading to the loss of phytoplankton on which flamingos feed making them to migrate. A strong positive correlation between conductivity and the lesser flamingo population was noticed signifying that low conductivity affects the growth of phytoplankton and since the lesser flamingos depend on the phytoplankton for their feed, this subsequently revealed that the phytoplankton density could be a notable predictor of the lesser flamingo occurrence in Lake Nakuru. There was also a strong positive correlation noticed between phytoplankton and the lesser flamingo population which confirms that feed availability is a key determining factor of the lesser flamingo distribution in the lake. It is projected that there would be an increment in temperatures, rainfall and evaporation for the period 2017 to 2100 under RCP2.6 and RCP8.5 relative to the baseline period 1971 to 2000 obtained from the Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model ensemble. As a result, it is expected that the lake will further increment in surface area and depth by the year 2100 due to increased rainfall thereby affecting the populations of the lesser flamingos and phytoplankton, as the physicochemical factors of the lake will alter as well during the projected period.

scholarly journals Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change

2020 ◽  
Vol 14 (9) ◽  
pp. 3155-3174 ◽  
Author(s):  
Eleanor J. Burke ◽  
Yu Zhang ◽  
Gerhard Krinner
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
High Latitude ◽  
Soil Profile ◽  
Coupled Model ◽  
The Arctic ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Northern High Latitude ◽  
Intercomparison Project

Abstract. Permafrost is a ubiquitous phenomenon in the Arctic. Its future evolution is likely to control changes in northern high-latitude hydrology and biogeochemistry. Here we evaluate the permafrost dynamics in the global models participating in the Coupled Model Intercomparison Project (present generation – CMIP6; previous generation – CMIP5) along with the sensitivity of permafrost to climate change. Whilst the northern high-latitude air temperatures are relatively well simulated by the climate models, they do introduce a bias into any subsequent model estimate of permafrost. Therefore evaluation metrics are defined in relation to the air temperature. This paper shows that the climate, snow and permafrost physics of the CMIP6 multi-model ensemble is very similar to that of the CMIP5 multi-model ensemble. The main differences are that a small number of models have demonstrably better snow insulation in CMIP6 than in CMIP5 and a small number have a deeper soil profile. These changes lead to a small overall improvement in the representation of the permafrost extent. There is little improvement in the simulation of maximum summer thaw depth between CMIP5 and CMIP6. We suggest that more models should include a better-resolved and deeper soil profile as a first step towards addressing this. We use the annual mean thawed volume of the top 2 m of the soil defined from the model soil profiles for the permafrost region to quantify changes in permafrost dynamics. The CMIP6 models project that the annual mean frozen volume in the top 2 m of the soil could decrease by 10 %–40 %∘C-1 of global mean surface air temperature increase.

scholarly journals Simulated Tropical Precipitation Assessed across Three Major Phases of the Coupled Model Intercomparison Project (CMIP)

2020 ◽  
Vol 148 (9) ◽  
pp. 3653-3680 ◽  
Author(s):  
Stephanie Fiedler ◽  
Traute Crueger ◽  
Roberta D’Agostino ◽  
Karsten Peters ◽  
Tobias Becker ◽  
...  
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
Climate Change Signal ◽  
Positive Trend ◽  
Model Intercomparison ◽  
Tropical Precipitation ◽  
Intercomparison Project

Abstract The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.

scholarly journals The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

2015 ◽  
Vol 19 (10) ◽  
pp. 4055-4066 ◽  
Author(s):  
A. Gobiet ◽  
M. Suklitsch ◽  
G. Heinrich
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Bias Correction ◽  
Climate Change Signal ◽  
Model Errors ◽  
Statistical Bias ◽  
Data Set ◽  
Dependent Model ◽  
Temperature Climate ◽  
Statistical Bias Correction

Abstract. This study discusses the effect of empirical-statistical bias correction methods like quantile mapping (QM) on the temperature change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model data set by up to 15 %. Such modification is currently strongly discussed and is often regarded as deficiency of bias correction methods. However, an analytical analysis reveals that this modification corresponds to the effect of intensity-dependent model errors on the CCS. Such errors cause, if uncorrected, biases in the CCS. QM removes these intensity-dependent errors and can therefore potentially lead to an improved CCS. A similar analysis as for the multi-model mean CCS has been conducted for the variance of CCSs in the multi-model ensemble. It shows that this indicator for model uncertainty is artificially inflated by intensity-dependent model errors. Therefore, QM also has the potential to serve as an empirical constraint on model uncertainty in climate projections. However, any improvement of simulated CCSs by empirical-statistical bias correction methods can only be realized if the model error characteristics are sufficiently time-invariant.

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