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Background Mosquitoes are key vectors for the transmission of several diseases. Anopheles gambiae is known to transmit pathogens of malaria and filariasis. Due to several anthropogenic factors such as climate change and population growth leading to diverse land use, their distribution and disease spreading pattern may change. This study estimated the potential distribution and climatic suitability of An. gambiae under the present-day and future conditions across Southwest Nigeria using Ecological Niche Modelling (ENM). The future scenarios assessed were based on two general circulation models (GCMs), namely community climate system model 4 (CCSM4) and geophysical fluid dynamics laboratory-climate model 3 (GFDL-CM3), in two representative concentration pathways (RCP 2.6 and RCP 8.5). Methodology The occurrence data were obtained from literatures that have reported the presence of An. gambiae mosquito species in locations within the study area. Ecological niche modelling data were processed and analysed using maximum entropy algorithm implemented in MaxEnt. Result Fifty-five (55) unique occurrences of An. gambiae were used in the model calibration after data cleaning. Data analysis for the present-day habitat suitability shows that more than two-thirds (81.71%) of the study area was observed to be suitable for An. gambiae population. However, the two future GCMs showed contrasting results. The CCSM4 models indicated a slight increase in both RCPs with 2.5 and 8.5 having 81.77 and 82.34% suitability, respectively. The reverse was the case for the GFDL-CM3 models as RCPs 2.5 and 8.5 had 78.86 and 76.86%. Conclusion This study revealed that the study area is climatically suitable for An. gambiae and will continue to be so in the future irrespective of the contrasting results from the GCMs used. Since vector population is often linked with their disease transmission capacity, proper measures must be put in place to mitigate disease incidences associated with the activities of An. gambiae.

This study suggests developing a new drought index using the conditional probability of precipitation associated with a wide range of other weather variables, such as temperature, to monitor droughts. Once the marginal probability distributions of the variables become known, it is possible to determine the joint and conditional probabilities through the choice of suitable copula functions. This new drought index of the so-called conditional standardized precipitation index (CSPDI) allows for evaluation of droughts just like the standardized precipitation index (SPI). The L-Moment method was used in the application part of the study to estimate the marginal probabilities of temperature and precipitation; on the other hand, both French-Gumbel Morgenstein (FGM) and Gaussian copula types were used within the case studies to assess joint and conditional probabilities. To evaluate the potential of the proposed drought index, drought patterns were reviewed through this index across several climate zones obtained through the Holdridge Life Zone (HLZ) method. To demonstrate the proposed approach’s capabilities regarding climate change studies, the CSPDI has been applied to the production of the Community Climate System Model (CCSM-4) selected near the major provinces in the country that stand out in population, agriculture, and industrial development. In addition to this, the kriging interpolation technique has been used to embed temperature and precipitation variables recorded by the country’s meteorological stations within the same grid points as the CCSM-4 models. The spatial analysis of the drought patterns using the results of the CCSM4 models and the weather stations established at grid points in the vicinity of the five largest cities of the country provide a useful basis in terms of measuring drought conditions. All these results showed that the CSPDI results changed significantly according to the time characteristics; choosing the temperature as a secondary variable in the CSPDI calculation did not affect the drought model of a rainy weather station such as Rize. But in semi-arid regions such as Izmir and Antalya, both coastal regions, the temperature had a strong effect on drought patterns. Furthermore, the suggested methodology has been applied to the results of GCM models used with CCSM-4 built on the RCP 8.5 scenario. The precipitation and temperatures used in the CSPDI calculations were derived from the NCAR GIS climate model, which allowed the IPCC to apply the CCSM-4 simulation methods in the fifth assessment of the AR5 report.

The present study used the maximum entropy (MaxEnt) model to predict the potential distribution of Clerodendrum infortunatum L. under current climatic conditions and future distribution under Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios of the Community Climate System Model (CCSM, version 4) for 2050 and 2070 in Dehradun district, India. C. infortunatum L. is native shrub species used in the traditional medicinal system in India due to antioxidant, antimicrobial, anti-malaria, anthelmintic, and analgesic properties. Results showed that the MaxEnt model was accurate, with the area under ROC (Receiver Operating Characteristic) curve (AUC) being 0.837 with precipitation of coldest quarter and elevation as major contributing variables to the model. The study found that areas totaling 200.4 km2 are currently highly suitable for C. infortunatum L., which will decrease by 56.9 km2 by 2070 in the RCP 2.6 scenario and to 23.7 km2 by 2070 in the RCP 8.5 scenario. Prediction of the suitable habitat for the species under climate change scenarios could help decision-makers understand the distribution of the species and prepare strategies for its scientific management.

The regional Climate-Weather Research and Forecasting model (CWRF) was used to downscale the NCAR Community Climate System Model V4.0 (CCSM4) projection of China precipitation changes from the present (1974–2005) to future (2019–2050) under the high emission scenario RCP8.5. The CWRF downscaling at 30-km improved CCSM4 in capturing observed key precipitation spatiotemporal characteristics, correcting rainband dislocations, seasonal-mean biases, extreme-rainfall underestimates and rainy-day overestimates. For the future, CWRF generally reduced CCSM4 projected changes in magnitude, producing still significant increases mostly in summer for mean precipitation in the Northeast, North China and Southwest and for extreme precipitation in North China, South China and the Southwest. These regional precipitation increases were direct responses to enhanced ascending motions and moisture transports from adjacent oceans as the east Asian jet shrunk westward and the Hadley circulation widened northward under global warming. The identification of such robust physical mechanisms added confidence in the CWRF downscaled regional precipitation changes. Furthermore, the CWRF downscaling corrections were systematically carried from the present into future, accounting for projection uncertainties up to 40%. Regional biases, however, could not be simply removed from projected changes because their correspondences were strongly nonlinear, highlighting CWRF’s ability to project more reliable changes by reducing model structural uncertainties.

Tropical cyclones (TCs) that make landfall over India’s east coast are responsible for significant loss of life along affected coastlines. TCs forming over the Bay of Bengal (BoB) region in October, November, and December have, in the past, intensified significantly at the time of landfall. The effects of climate change on TCs of different strengths and their characteristics such as track, intensity, precipitation, and convective available potential energy over the BoB region have not been well studied. This study explores the effects of climate change on two TCs of very severe cyclonic storm (VSCS) category (TC Vardah and TC Madi), and two other TCs of extremely severe cyclonic storm (ESCS) category (TC Hudhud and TC Phailin) formed over BoB, both in the short term (2035) and long term (2075). The high-resolution Weather Research and Forecasting (WRF) model is used to simulate the TCs under current and future climate conditions. The simulated TC track and intensity in the current climate agree well with the observations. To explore the impacts of climate change on TCs, the mean climate change signal, computed from future projections of the Community Climate System Model (CCSM4) in different representative concentration pathway (RCP) scenarios, is added to current climate conditions by using the pseudo global warming method. Results show a climate change-related reduction in TC translation speed, deepening of TC core, increased maximum surface wind, and increased precipitation over land in future RCP (4.5, 6.0, and 8.5) scenarios. The TCs in future RCP scenarios are seen to be more intensified compared to current climate simulations. Results demonstrate that all VSCS and ESCS category TCs considered in this study are likely to further intensify to the next higher category level with respect to their current classification, particularly in the far future RCP 6.0 and in the far future RCP 8.5 scenarios. The cyclone damage potential index of TC Vardah, TC Hudhud, and TC Phailin is projected to increase in a future warming climate.

Significant uncertainties exist in climate change projections, and these mainly originate from the internal variability of the climate system. Using 40-member ensemble projections of the National Center for Atmospheric Research Community Climate System Model Version 3, we explored the uncertainties in the projected change of the Northeast Asian summer precipitation during 2006–2060 and examined the sources of the internal variability-generated uncertainties. The projected summer precipitation trends show large diversities from member to member, attributable to the superposition of the internal climate variability on the external forcing. Except for Northeast China, the signal-to-noise ratios (SNRs) are less than one, indicating the internally induced precipitation trends dominate the externally forced trends over large parts of Northeast Asia. The first mode of the internally induced precipitation trends displays widespread negative trends over Northeast Asia, and the second mode is characterized by a dipole trend pattern between Northeast China and Siberia. The atmospheric circulation trend patterns related to the first and second modes bear resemblances to those related to the Arctic Oscillation and Polar-Eurasian pattern, respectively. A dynamical adjustment was further performed on the summer precipitation trends to reduce the impact of the internal atmospheric variability. After the dynamical adjustment, the level of similarity in the residual precipitation trends across the 40 ensemble members increased significantly. In particular, the SNRs increased notably, with values larger than one over a vast area of Northeast Asia, which was in sharp contrast to values larger than one only being noticeable within Northeast China prior to the adjustment.

The present study evaluates the performance of Conformal-Cubic Atmospheric Model (CCAM) simulations downscaled from six global climate models (GCMs) (i.e., ACCESS1.0, CNRM-CM5, CCSM4, GFDL-CM3, MPI-ESM-LR, and NorESM-M) and Max Plank’s Regional Model (REMO2009(MPI)) obtained from the South-Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) for analyzing the summer monsoon maximum temperature (Tmax) over the fifteen Agro-Climatic Zones (ACZs) in India. The model simulations are compared with the two sets of observed data obtained from the India Meteorology Department (IMD) and Climate Research Unit (CRU) for the period from 1981 to 2005. The results illustrate that the skill of CCAM regional climate models (RCMs) is higher than the REMO in simulating the Tmax over all the regions. The spatial patterns of Tmax in CCAM (CCSM) and CCAM (CNRM) are closer to IMD, while the Tmax distributions in CCAM (CNRM), CCAM (CCSM), and CCAM (BCCR) agree well with the CRU, and correlation coefficient (CC) is more than 0.6; however, large positive biases in all RCMs are depicted over the Himalayan regions. The inter-comparison among all the RCMs suggest that the CCAM (CNRM) and CCAM (CCSM) are rendering as the foremost models in simulating Tmax over different ACZs. Performances of these two models also infer the usefulness of the model products for impact studies over the individual ACZs. However, the existing systematic biases in the RCMs impeded the model performance and it is necessary to remove the model bias prior to some real-time application. In this study, two bias correction methods, i.e., linear scaling (LS) and distribution mapping (DM), have been used to correct RCM output bias. It is found that the model performance using DM correction is better than LS method. The performance validations are evaluated based on the probability density function (PDF), CC, and standard deviation (SD) with 95% confidence level. The model evaluation has also been justified using mean absolute error (MAE) index, Nash-Sutcliffe coefficient (NS) index, percent bias (Pbias), and the Willmott’s index of agreement (d) which confirm the research findings. The results are providing an effective guidance on the usefulness of bias corrected RCMs over a particular ACZs for impact assessment.

Climate models predict that the Mediterranean region will experience a particularly marked increase in aridity during the 21st century when compared to other regions. This study examines the current and future potential geographical distribution of the stone pine (Pinus pinea L.), a species of considerable ecological and economic importance, in the light of aridity predictions in the Mediterranean region. For this purpose, 19 bioclimatic variables taken from the Worldclim database in 30 arc-second spatial resolution (∼1000 m) were used. The bioclimatic variables that displayed high correlations with one another when applied to 125 pieces of presence data pertaining to the species were reduced using Principal Component Analysis (PCA) and the current and future potential distribution areas of the species were identified using MaxEnt 3.4.1 software. To determine the future geographical distribution of the species under the impact of climate change, the Community Climate System Model (CCSM ver. 4) was used to model the potential geographical distribution areas for two climate scenarios – Representative Concentration Pathways (RCP) 4.5 and 8.5 – and two time periods (2041–2060 and 2061–2080). According to the model data, the predicted potential geographical distribution areas of the stone pine increase under the 2041–2060 and 2061–2080 RCP 4.5–8.5 scenarios and move northwards and towards higher altitudes. Knowledge of the current and future potential distribution maps of the species, and especially of the suitable/very suitable distribution areas, can be used in work on land use, afforestation, ecological restoration and the protection of the species.

Due to global warming, the existing IDF curves that were derived from historical data may no longer valid for estimating future rainfall. On the other hand, the alternative future rainfall data generated from Community Climate System Model version 3 (CCSM3) cannot be used directly due to systematic biases caused by non-inclusion of local features. Quantile mapping was used to bias correct the rainfall data generated by CCSM3 model based on the properties of observed rainfall. Only 4 out of 16 stations in the state of Kelantan have shown an increasing significant trend for 1-h annual maximum rainfall. The best transfer function used to correct bias in CCSM3 data is the second order polynomial function. The quantile mapping for bias correction is satisfactory as the highest RMSE was only 4.045, a reduction by 15% compared to the original CCSM3 data. Subsequently, a unique transfer function was developed to represent the behavior of CCSM3 and observed data for each station. The future intensity was found to increase for short and medium return periods of 2-, 5-, 10- and 25-year, where most of the stations have Climate Change Factor (CCF) larger than 1. On the contrary, for a longer return period of 50- and 100-year, the intensities are predicted to be lower.

Pest growth is closely related to the climate conditions. This study aimed to analyze the impact of climate variability and climate change on the potential attack of green leafhopper (Empoasca sp.) on tea plantations at PTPN VIII Gunung Mas. The analysis was carried out to calculate the value of Ecoclimatic Index (EI) based on the functions of the compare years and the compare location in CLIMEX model. Pest suitability in the future was projected using RCP 4.5 and 8.5 climate scenarios, which were derived from MIROC 5 and CCSM 4 climate model outputs. The result indicated that Gunung Mas Tea Plantation was suitable for Empoasca sp. growth. The EI value (58) in the baseline year (2012-2017) confirmed the suitability. Climate variability influences the suitability for Empoasca sp. growth. During El-Niño, the EI value decrease substantially (~26%). On the other hand, the EI value is projected to slightly increase in the future for both climate scenarios.

Swietenia macrophylla is an economically important tree species propagated by seeds that lose their viability in a short time, making seed germination a key stage for the species recruitment. The objective of this study was to determine the cardinal temperatures and thermal time for seed germination of S. macrophylla, and its potential distribution under different climate change scenarios. Seeds were placed in germination chambers at constant temperatures from 5 to 45 °C and their thermal responses modelled using a thermal time approach. In addition, the potential biogeographic distribution was projected according to the Community Climate System Model version 4 (CCSM4). Germination rate reached its maximum at 37.3 ± 1.3 °C (To), seed germination decreased to near zero at 52.7 ± 2.2 °C (ceiling temperature, Tc) and at 12.8 ± 2.4 °C (base temperature, Tb). The suboptimal thermal time θ150 needed for 50% germination was ca. 190 °Cd, which in the current scenario is accumulated in 20 days. The CCSM4 model estimates an increase of the potential distribution of the species of 12.3 to 18.3% compared to the current scenario. The temperature had an important effect on the physiological processes of the seeds. With the increase in temperature, the thermal needs for germination are completed in less time, so the species will not be affected in its distribution. Although the distribution of the species may not be affected, it is crucial to generate sustainable management strategies to ensure its long-term conservation.

Species tend to shift their suitable habitat both altitudinally and latitudinally under climate change. Range shift in plants brings about habitat contraction at rear edges, forcing leading edge populations to explore newly available suitable habitats. In order to detect these scenarios, modeling of the future geographical distribution of the species is widely used. Vachellia negrii (Pic.-Serm.) Kyal. &amp, Boatwr. is endemic to Ethiopia and was assessed as vulnerable due to changes to its habitat by anthropogenic impacts. It occurs in upland wooded grassland from 2000–3100 m.a.s.l. The main objective of this study is to model the distribution of Vachellia negrii in Ethiopia by using Maxent under climate change. Nineteen bioclimatic variables were downloaded from an open source. Furthermore, topographic position index (tpi), solar radiation index (sri) and elevation were used. Two representative concentration pathways were selected (RCP 4.5 and RC P8.5) for the years 2050 and 2070 using the Community Climate System Model (CCSM 5). A correlation analysis of the bioclimatic variables has resulted in the retention of 10 bioclimatic variables for modeling. Forty-eight occurrence points were collected from herbarium specimens. The area under curve (AUC) is 0.94, indicating a high-performance level of the model. The distribution of the species is affected by elevation (26.4%), precipitation of the driest month (Bio 14, 21.7%), solar radiation (12.9%) and precipitation seasonality (Bio15, 12.2%). Whereas the RCP 8.5 has resulted in decrease of suitable areas of the species from the current 4,314,153.94 ha (3.80%) to 4,059,150.90 ha (3.58%) in 2050, this area will shrink to 3,555,828.71 ha in 2070 under the same scenario. As climate change severely affects the environment, highly suitable areas for the growth of the study subject will decrease by 758,325 ha. The study’s results shows that this vulnerable, endemic species is facing habitat contraction and requires interventions to ensure its long-term persistence.

Cunninghamia konishii Hayata is a rare and endangered plant species that plays a relevant role in ecological andcommercial systems of natural forests in Vietnam. In this research, we evaluated the potential geographic distribution ofC. konishii under current and future climatic conditions in Northern Vietnam using the ecological niche modelling approachbased on the largest available database of occurrence records for this species. C. konishii is mainly distributed inthe northern part of Vietnam at altitudes above 1000 m where the slopes range between 12 and 25 degrees, particularlyin special-use and protected forest. The optimal distribution area of C. konishii requires specific climatic conditions: anannual precipitation around 1200 mm, precipitation of the warmest quarter ranging from 600 to 800 mm, a precipitationseasonality of 90 to100 mm, an annual mean temperature ranging from 12°C to 19°C, and a temperature seasonalityranging from 300 to 350. Additionally, the species requires specific soil groups: humic acrisols, ferralic acrisols, andyellow-red humic soils. Considering these requirements, the results of our research show that the suitable regions for thegrowth of C. konishii are found in the provinces of Ha Giang, Son La, Thanh Hoa and Nghe An, covering a total area of1509.56 km2. However, analyzing the results under the Community Climate System Model version 4 (CCSM4) model, itis possible to observe that the area will decline to 504.39 km2 by 2090 according to RCP 2.6 scenario, to 406.25 km2 inthe RCP 4.5 scenario, and to 47.62 km2 in the RCP 8.5 scenario. The findings of this present research may be applied toseveral additional studies such as identifying current and future locations to establish conservation areas for C. konishii.

Precipitation is critical for maintaining the stability of an ecosystem, especially in arid regions. This study primarily focuses on climatic changes during present (from 1985 to 2005) and future (from 2040 to 2059) periods in Xinjiang, Northwest China. In this study, the Weather Research and Forecasting model is implemented in Xinjiang to efficiently predict the future climate. Moreover, the National Climate Research Center Community Climate System Model version 4 is employed for the mid-21st century under representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively). Our results indicate that the amount of annual precipitation will increase in the future under RCP4.5 and RCP8.5 in Xinjiang, especially in mountainous areas. The increase in precipitation is predicted to be much smaller under RCP8.5 than under RCP4.5, except in Southern Xinjiang. Moreover, the increasing precipitation predicted in Xinjiang implies that the current humid and warm conditions will persist, thereby further indicating that Xinjiang is still currently suffering from a dry climate. The largest increase in seasonal precipitation is predicted to occur in spring and summer in Tianshan and Northern Xinjiang, whereas this phenomenon is predicted to occur in spring and winter in Southern Xinjiang. In addition, it is predicted that daily heavy precipitation events will occur more frequently in various subregions of Xinjiang, although light rain events will remain dominant. Finally, the relative humidity is closely related to the changes in annual and seasonal precipitation.

This study aims to determine how the distribution of Carpinus betulus L. (Linnaeus 1763) has changed from LGM to the present and the future. For this purpose, the species fossil pollen data, occurrence data, and bioclimatic variables obtained from WorldClim were used in this study. Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5 scenarios and Community Climate System Model (CCSM4) model were used for future projection. The PCA method was applied to these variables, and nine variables were determined for species distribution models (SDMs). Models were produced with MaxEnt 3.4.1, and ArcGIS 10.5. Among the nine bioclimatic variables, the BIO18 parameter had the highest contribution to the resulting prediction pattern. The accuracy of the models was measured as 0.83 with the AUC test and 0.80 with the TSS test. According to the obtained results, the most suitable distribution areas of Carpinus betulus in the Last Glacial Maximum (LGM) were the Black Sea region and Western Anatolia. In the future, distribution patterns of Carpinus betulus may shift to the north by decreasing the available present distribution areas in Anatolia and its surroundings. When the models produced according to different climate scenarios for the future are examined, it is predicted that the species will preserve most of the distribution areas in Anatolia according to the RCP 2.6, and will lose most of its distribution areas based on the RCP 4.5. The suitable distribution area will almost disappear according to the RCP 8.5. Hence, these results should be taken into account to improve conservation and management plans for Carpinus betulus and considering biodiversity, and model results will make an important contribution to the future distribution of hornbeams habitat in Anatolia and its surroundings.

New methods are presented for determining the role of coupled ocean-atmosphere model climate bias on the strength and variability of the El Nino-Southern Oscillation (ENSO) and on the seasonal ensemble prediction of El Nino and La Nina events. An intermediate complexity model with a global atmosphere coupled to a Pacific basin ocean is executed with parallelised algorithms to produce computationally efficient year-long forecasts of large ensembles of coupled flow fields, beginning every month between 1980 and 1999. Firstly, the model is provided with forcing functions that reproduce the average annual cycle of climatology of the atmosphere and ocean based on reanalysed observations. We also configure the model to generate realistic ENSO fluctuations. Next, an ensemble prediction scheme is employed which produces perturbations that amplify rapidly over a month. These perturbations are added to the analyses and give the initial conditions for the ensemble forecasts. The skill of the forecasts is presented and the dependency on the annual and ENSO cycles determined. Secondly, we replace the forcing functions in our model with functions that reproduce the averaged annual cycles of climatology of two state of the art, comprehensive Coupled General Circulation Models. The changes in skill of subsequent ensemble forecasts elucidate the roles of model bias in error growth and potential predictability. References C. S. Frederiksen, J. S. Frederiksen, and R. C. Balgovind. ENSO variability and prediction in a coupled ocean-atmosphere model. Aust. Met. Ocean. J., 59:35–52, 2010a. URL http://www.bom.gov.au/jshess/papers.php?year=2010. C. S. Frederiksen, J. S. Frederiksen, and R. C. Balgovind. Dynamic variability and seasonal predictability in an intermediate complexity coupled ocean-atmosphere model. In Proceedings of the 16th Biennial Computational Techniques and Applications Conference, CTAC-2012, volume 54 of ANZIAM J., pages C34–C55, 2013a. doi:10.21914/anziamj.v54i0.6296. C. S. Frederiksen, J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough. Trends and projections of Southern Hemisphere baroclinicity: the role of external forcing and impact on Australian rainfall. Clim. Dyn., 48:3261–3282, 2017. doi:10.1007/s00382-016-3263-8. J. S. Frederiksen, C. S. Frederiksen, and S. L. Osbrough. Seasonal ensemble prediction with a coupled ocean-atmosphere model. Aust. Met. Ocean. J., 59:53–66, 2010b. URL http://www.bom.gov.au/jshess/papers.php?year=2010. J. S. Frederiksen, C. S. Frederiksen, and S. L. Osbrough. Methods of ensemble prediction for seasonal forecasts with a coupled ocean-atmosphere model. In Proceedings of the 16th Biennial Computational Techniques and Applications Conference, CTAC-2012, volume 54 of ANZIAM J., pages C361–C376, 2013b. doi:10.21914/anziamj.v54i0.6509. P. R. Gent, G. Danabasoglu, L. J. Donner, M. M. Holland, E. C. Hunke, S. R. Jayne, D. M. Lawrence, R. B. Neale, P. J. Rasch, M. Vertenstein, P. H. Worley, Z.-L. Yang, and M. Zhang. The community Climate System Model version 4. J. Clim., 24:4973–4991, 2011. doi:10.1175/2011JCLI4083.1. S. Grainger, C. S. Frederiksen, and X. Zheng. Assessment of modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP5 models. J. Clim., 27:8107–8125, 2014. doi:10.1175/JCLI-D-14-00251.1. E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph. The NCEP/NCAR 40-year reanalysis project. B. Am. Meteorol. Soc., 77:437–472, 1996. doi:10.1175/1520-0477(1996)0772.0.CO;2. H. A. Rashid, A. Sullivan, A. C. Hirst, D. Bi, X. Zhou, and S. J. Marsland. Evaluation of El Nino-Southern Oscillation in the ACCESS coupled model simulations for CMIP5. Aust. Met. Ocean. J., 63:161–180, 2013. doi:10.22499/2.6301.010. K. E. Taylor, R. J. Stouffer, and G. A. Meehl. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 93:485–498, 2012. doi:10.1175/BAMS-D-11-00094.1.

Previous studies show that the evolution of global mean temperature forced by the total forcing is almost the same as the sum of individual orbital, ice sheet, greenhouse gas and meltwater single forcing runs in the last 12 000 years in three independent climate models: Community Climate System Model 3 (CCSM3), Fast Met Office/UK Universities Simulator (FAMOUS) and Loch-Vecode-Ecbilt-Clio-Agism Model (LOVECLIM). This validity of the linear response is useful because it simplifies the interpretation of the climate evolution. However, it has remained unclear if this linear response is valid on other spatial and temporal scales and, if valid, in what regions. Here, using a set of TraCE-21ka (Simulation of the Transient Climate of the Last 21,000 Years) climate simulations, the spatial and temporal dependence of the linear response of the surface temperature evolution in the Holocene is assessed approximately using the correlation coefficient and a linear error index. The results show that the response of global mean temperature is almost linear on orbital, millennial and centennial scales in the Holocene but not on a decadal scale. The linear response differs significantly between the Northern Hemisphere (NH) and Southern Hemisphere (SH). In the NH, the response is almost linear on a millennial scale, while in the SH the response is almost linear on an orbital scale. Furthermore, at regional scales, the linear responses differ substantially between the orbital, millennial, centennial and decadal timescales. On an orbital scale, the linear response is dominant for most regions, even in a small area of a midsize country like Germany. On a millennial scale, the response is still approximately linear in the NH over many regions. Relatively, the linear response is degenerated somewhat over most regions in the SH. On the centennial and decadal timescales, the response is no longer linear in almost all the regions. The regions where the response is linear on the millennial scale are mostly consistent with those on the orbital scale, notably western Eurasian, North Africa, subtropical North Pacific, the tropical Atlantic and the Indian Ocean, likely causing a large signal-to-noise ratio over these regions. This finding will be helpful for improving our understanding of the regional climate response to various climate forcing factors in the Holocene, especially on orbital and millennial scales.

Sea surface temperature errors that develop after 1 week are investigated as a function of resolution using retrospective forecasts from the Community Climate System Model. One version resolves the ocean and atmosphere to approximately 1° while the second version resolves the ocean to 0.1° and the atmosphere to 0.5°. The forecasts are initialized on January 1 from 1982 to 2003. The spatial pattern of the Pacific basin sea surface temperatures errors after 1 week is mostly similar at both resolutions, with the exception of the coast of South America. The coastal ocean surface cools within the higher-resolution simulations but warms within the lower-resolution simulations. The difference in the ocean surface temperature is instead attributed to differing changes in the upwelling. Coastal upwelling increases within the higher-resolution simulation, increasing the lower tropospheric stability and encouraging the cloud cover. In contrast, the upwelling decreases within the lower-resolution simulations at 27°S, allowing the ocean surface to warm in spite of cooling from the atmosphere. In both simulations, the northward winds and surface currents weaken, because the South Pacific sea level pressure high moves westward. The increased oceanic upwelling in the high-resolution simulation is instead attributed to an increase in the westward zonal currents. The high-resolution model resolves the narrow Humboldt current, while the low-resolution model does not. This study demonstrates that the processes responsible for SST errors in eastern upwelling boundary current regions change when the oceanic grid spacing becomes fine enough to allow resolution of the oceanic boundary currents.

A downscaled projection over the Peninsular Florida (PF) region is conducted with a Regional Climate Model (RCM) at 10 km grid spacing that incorporates interactive coupling between the atmosphere and ocean components of the climate system. This is first such application of a coupled ocean-atmosphere model for climate projection over the PF region. The RCM is shown to display reasonable fidelity in simulating the mean current climate and exhibits higher variability both in the ocean and in the atmosphere than the large-scale global model (Community Climate System Model version 4 [CCSM4]), which is used to drive the RCM. There are several features of the regional climate that RCM displays as an improvement over CCSM4: upper ocean thermal stratification, surface eddy kinetic energy of the ocean, volume flux through the Yucatan Channel, and terrestrial rainfall over PF. The projected mean hydroclimatic change over the period 2041–2060 relative to 1986–2005 over PF shows significant difference between RCM and CCSM4, with the RCM becoming significantly drier and CCSM4 moderately wetter. Furthermore, over the ocean surface, especially over the West Florida Shelf (WFS), RCM displays a wetter and a warmer surface climate compared to the CCSM4 simulation. Our analysis of the model output indicates that improved resolution of ocean bathymetry in the RCM plays a significant role in the response of the projected changes in surface heat flux, clouds, upper ocean circulations and upper ocean stratification, which manifests with some of the largest differences from the CCSM4 projections, especially over the shallower parts of the ocean around PF. This contrast is most apparent between WFS and PF in the RCM simulation, which suggests that a future warm climate would likely produce more rain over WFS at the expense of corresponding reduction over PF, contrary to the absence of any such gradient in the CCSM4 simulation. Furthermore, in the RCM simulation, the warming of the sub-surface ocean in the future climate is owed to the combined influence of excess atmospheric heat flux directed towards the ocean from the atmosphere and the advective heat flux convergence with the relative slowing of the Loop Current in the future climate. The study demonstrates that such RCMs with coupled ocean-atmosphere interactions are necessary to downscale the global climate models to project the surface hydro-climate over regions like PF that have mesoscale features in the ocean, which can influence the terrestrial climate.

The study of the impact on climate change on water resources provides useful information for long-term trend analyses of important issues such as control of floods, management of drought, agricultural production, etc. This work is an attempt to assess the flood events in Vaigai watershed, Tamil Nadu (India). The Mann–Kendall test was performed to assess the rainfall and temperature trends on data extracted from Climatic Research Unit (CRU) and Community Climate System Model 4 (CCSM4) model for historical and future scenarios. The CCSM4 model was compared with India Meteorological Department and CRU data sets to analyze the performance and consistency of model data among the years of flood, viz., 1993, 2010, and 2015. The CCSM4 model was able to capture, in a few instances, the historical as well as future flood events over the region. The maximum rainfall (738.11 mm) was predicted for 2021 followed by 2038 and 2040, and the lowest rainfall (43.40 mm) was predicted for 2036. Besides, the temperature increased by 1 °C and rainfall was mostly maximum in September corresponding to the south-west monsoon (SWM) season. The predicted increases in rainfall can result in flash floods, which have serious implications on the agricultural sector and water resources of the basin, while the decreasing rainfall during the other seasons helps to reduce the flood severity.

In order to plan for agricultural irrigation, the drought risk and amount of water needed for crops must be well studied. In this work, we apply the Standard Precipitation Index (SPI) and Crop Water Need (CWN) using input data from a fine-resolution Nested Regional Climate Model (NRCM) to assess the risk of future agricultural drought in Mainland Southeast Asia from 2020 to 2029. The NRCM was performed with resolutions of 60 and 10-km grid spacing for the present (1990–1999) and the future (2020–2029). The model employs initial and boundary conditions from the Community Climate System Model Version 4 (CCSM4) for meteorological variables. Two simulations, present-day (1990–1999) and future (2020–2029), were conducted under the Representative Concentration Pathway (RCP) 8.5 climate scenario. In general, the comparison between the NRCM predictions and observed data shows that the NRCM reasonably predicts precipitation and 2-m temperature with a high correlation of 0.89–0.98 and index of agreement (IOA) values ranging from 0.76 to 0.95. The future precipitation tends to decrease by (−1)–(1) mm/day, while the temperature will increase by up to 2–3 °C, which are favorable conditions for drought risk. Additionally, the SPI values between (− 1.5) and 0 for both the dry and rainy seasons indicate a high possibility of drought events in the future. There seemed to be some evidence of drought risk in this region, but the calculation of CWN indicates that the region will remain relatively water rich for agriculture.

The regional climate of the Tibetan Plateau (TP) was simulated by dynamically downscaling reanalysis data and the Community Climate System Model version 4 (CCSM4) and comparing trends of temperature and precipitation with gridded observations. Then, future CCSM4 projections under high and low emission scenarios were downscaled and compared with CCSM4 projections. Observations showed a marked upward trend in temperature and precipitation since 1979. The spatiotemporal distribution of temperature and precipitation in the TP were well represented by the reanalysis data. Downscaled simulations of ERA-Interim and CCSM4 were able to reproduce the spatial distribution of temperature in the TP; however, a cold bias was apparent in the central and western regions. Compared with precipitation observations, the downscaled CCSM4 simulation showed markedly different precipitation trends. Future climate projections indicated that temperatures will increase markedly in the TP, especially under the high emission RCP8.5 scenario. Under RCP4.5, both CCSM4 and the downscaled simulation projected a 1.5 °C increase in annual temperatures during 2006–2050, while under RCP8.5, the downscaled simulation projected an increase greater than 2.5 °C, and CCSM4 projected an increase of 2.0 °C. Emission scenarios had no apparent impact on projections of future precipitation. Therefore, to mitigate warming over the TP, reducing greenhouse gas emission should be a priority.

The world is passing through abrupt climate changes that are a threat for biodiversity. Stryphnodendron adstringens (Mart.) Coville (Fabaceae) is a tree species endemic to the “Cerrado” biome with a high economic potential. Its exploitation is done in an extractive way, which, coupled with climate changes and other landscape changes, can contribute to its decline. Here, we use ecological niche modeling (ENM) to map its distribution and environmental suitability in the past, present and future climates. The environmental variables were derived from community climate system model, for present, past and future scenarios. The maps showed that the specie had a larger distribution area in the past, during the last glacial maximum, and it decreased mainly in the Amazon rainforest region; today, the species is found mainly in the center of “Cerrado.” For the different climatic scenarios predicted for the future and considering various levels of anthropogenic drivers for climate change, a drastic loss of climatically suitability area of S. adstringens is expected, which may compromise the viability of the species. Our ENMs can then be useful to better establish and delimit conservation actions for this important species in the “Cerrado” region.

The changes of the East Asian summer monsoon (EASM) in response to increased CO2 atmospheric forcing are analyzed using the Super-Parameterized Community Climate System Model version 4 (SP-CCSM4). In response to the global warming caused by the increased atmospheric CO2 concentration, the precipitation and circulation of the EASM intensify. These changes are explained by the westward extension of the western North Pacific subtropical high (WNPSH). The displacement of the WNPSH is caused by two mechanisms: (i) the increase of sea surface temperature and (ii) the reduction of latent heat flux over the South China Sea and adjacent western Pacific Ocean. The changes in the surface fluxes over the tropics induce a Gill-type anticyclonic circulation in the lower troposphere to the north of the heating anomaly and a Rossby wave train from the tropics into the mid-latitude Pacific Ocean. The westerly anomalies on the northern side of the anticyclone strengthen the southwesterly flow on the western edge of the WNPSH. This flow further affects the wind anomalies and moisture transport over East Asia. The Rossby wave train affects the large-scale circulation associated with the WNPSH.

A comparative analysis of two sets of downscaled simulations of the current climate and the future climate projections over Peninsular Florida (PF) and the West Florida Shelf (WFS) is presented to isolate the role of high-resolution air-sea coupling. In addition, the downscaled integrations are also compared with the much coarser, driving global model projection to examine the impact of grid resolution of the models. The WFS region is habitat for significant marine resources, which has both commercial and recreational value. Additionally, the hydroclimatic features of the WFS and PF contrast each other. For example, the seasonal cycle of surface evaporation in these two regions are opposite in phase to one another. In this study, we downscale the Community Climate System Model version 4 (CCSM4) simulations of the late twentieth century and the mid-twenty-first century (with reference concentration pathway 8.5 emission scenario) using an atmosphere only Regional Spectral Model (RSM) at 10 km grid resolution. In another set, we downscale the same set of CCSM4 simulations using the coupled RSM-Regional Ocean Model System (RSMROMS) at 10 km grid resolution. The comparison of the twentieth century simulations suggest significant changes to the SST simulation over WFS from RSMROMS relative to CCSM4, with the former reducing the systematic errors of the seasonal mean SST over all seasons except in the boreal summer season. It may be noted that owing to the coarse resolution of CCSM4, the comparatively shallow bathymetry of the WFS and the sharp coastline along PF is poorly defined, which is significantly rectified at 10 km grid spacing in RSMROMS. The seasonal hydroclimate over PF and the WFS in the twentieth century simulation show significant bias in all three models with CCSM4 showing the least for a majority of the seasons, except in the wet June-July-August (JJA) season. In the JJA season, the errors of the surface hydroclimate over PF is the least in RSMROMS. The systematic errors of surface precipitation and evaporation are more comparable between the simulations of CCSM4 and RSMROMS, while they differ the most in moisture flux convergence. However, there is considerable improvement in RSMROMS compared to RSM simulations in terms of the seasonal bias of the hydroclimate over WFS and PF in all seasons of the year. This suggests the potential rectification impact of air-sea coupling on dynamic downscaling of CCSM4 twentieth century simulations. In terms of the climate projection in the decades of 2041–2060, the RSMROMS simulation indicate significant drying of the wet season over PF compared to moderate drying in CCSM4 and insignificant changes in the RSM projection. This contrasting projection is also associated with projected warming of SSTs along the WFS in RSMROMS as opposed to warming patterns of SST that is more zonal and across the WFS in CCSM4.

Model projections of the near-future response to anthropogenic warming show compensation between meridional heat transports by the atmosphere (AHT) and ocean (OHT) that are largely symmetric about the equator1–3, the causes of which remain unclear. Here, using both the Coupled Model Intercomparison Project Phase 5 archive and Community Climate System Model version 4 simulations forced with Representative Concentration Pathway 8.5 to 2600, we show that this transient compensation—specifically during the initial stage of warming—is caused by combined changes in both atmospheric and oceanic circulations. In particular, it is caused by a southward OHT associated with a weakened Atlantic Meridional Overturning Circulation, a northward apparent OHT associated with an ocean heat storage maximum around the Southern Ocean, and a symmetric coupled response of the Hadley and Subtropical cells in the Indo-Pacific basin. It is further shown that the true advective OHT differs from the flux-inferred OHT in the initial warming due to the inhomogeneous responses of ocean heat storage. These results provide new insights to further our understanding of future heat transport responses, and thereby global climatic processes such as the redistribution of ocean heat. The compensation between atmospheric and oceanic heat transports under anthropogenic warming can be linked to the combined impact of Atlantic Meridional Overturning Circulation weakening, perturbations to Southern Ocean heat storage, and coupled responses of the Hadley and Subtropical cells.

The climate system can potentially switch from one stable state to another. The closer a system is to a bifurcation point (i.e., ‘tipping point’), the more likely it is that even small perturbations can force the system to experience a state shift, e.g., a collapsing Atlantic meridional overturning circulation (AMOC) and associated cooling in parts of the North Atlantic. Here, we present an abrupt state transition from a warm to a cold North Atlantic climate state with expanded sea ice during an orbitally forced transient Holocene simulation performed with the Community Climate System Model version 3. The state transition is associated with a weakening of the AMOC by about 33% in this simulation. The changing background climate induced by slow external orbital forcing plays an important role for the abrupt climate shift. The model allows the identification of regions and variables that play a key role for a potential climate transition and show early-warning signals. Increase in autocorrelation and standard deviation as well as trends in skewness especially for sea-surface salinity in the northern North Atlantic are identified as robust early-warning signals, whereas no early-warning signals are found in the time series of the AMOC stream function.

Significant changes have occurred in the Antarctic Peninsula (AP) including warmer temperatures, accelerated melting of glaciers, and breakup of ice shelves. This study uses the Weather Research and Forecasting model (WRF) forced by the Community Climate System Model 4 (CCSM) simulations to study foehn wind warming in AP. Weather systems responsible for generating the foehn events are two cyclonic systems that move toward and/or cross over AP. WRF simulates the movement of cyclonic systems and the resulting foehn wind warming that is absent in CCSM. It is found that the warming extent along a transect across the central AP toward Larsen C Ice Shelf (LCIS) varies during the simulation period and the maximum warming moves from near the base of leeward slopes to over 40 km away extending toward the attached LCIS. Our analysis suggests that the foehn wind warming is negatively correlated with the incoming air temperature and the mountain top temperature during periods without significant precipitation, in which isentropic drawdown is the dominant heating mechanism. On the other hand, when significant precipitation occurs along the windward side of AP, latent heating is the major heating mechanism evidenced by positive relations between the foehn wind warming and 1) incoming air temperature, 2) windward precipitation, and 3) latent heating. Foehn wind warming caused by isentropic drawdown also tends to be stronger than that caused by latent heating. Comparison of WRF simulations forced by original and corrected CCSM data indicates that foehn wind warming is stronger in the original CCSM forced simulation when no significant windward precipitation is present. The foehn wind warming becomes weaker in both simulations when there is significant windward precipitation. This suggests that model’s ability to resolve the foehn warming varies with the forcing data, but the precipitation impact on the leeward warming is consistent.

A Standardized Precipitation Evapotranspiration Index (SPEI) model, which uses the FAO-56 Penman–Monteith equation to calculate potential evapotranspiration, has been developed and is considered appropriate for drought monitoring and assessment. However, the correlation of SPEI to total water storage (TWS) across multiple timescales, derived from future climate scenarios, is still unclear for China. In this study, the correlation of SPEI on different timescales (1-, 3-, 6-, 12-, 24- and 48-month) to TWS projected by a global climate model [the Community Climate System Model, version 4 (CCSM4)] under two representative concentration pathway scenarios (RCP2.6 and RCP8.5) for the period 2021–2100 in China is analyzed. The results show that, in general, SPEI is highly correlated with CCSM TWS in most of China (especially in eastern China) at the 12-month timescale, and is therefore regarded as an adequate indicator for representing situations of water resources and evaluating hydrological droughts. At the 12-month timescale, the correlations of SPEI to CCSM TWS vary across different river basins; and the variations in the pattern of correlations are greater due to faster warming and greater precipitation under RCP8.5. It is hoped that this article will provide guidance on the use of SPEI for detecting the impacts of future climate change on drought severity in China.

Various reasons are there in failures of Intergovernmental Panel on Climate Change (IPCC) simulation model for prediction of climate change. For the better understanding of IPCC model’s failures by researchers, an improvement is qualitative and quantitative analysis is required and to be implemented. We come across a continuous crashes in simulation of Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4), while measuring the impact of ocean model parameter uncertainties on weather simulations, during the period of uncertainty quantification (UQ) ensemble. This manuscript analyse the different machine learning algorithms, such as, Random forest, Linear Regression, k-means and naïve-bayes algorithms. From machine learning, a quality classifier called support vector machine (SVM) classification is used to predict and quantify the failures probability as a function of the values of POP2 parameters. Apart from quantification and prediction, this method performs a better understanding in simulation crashes in other complex geo-scientific models.

Proxy records suggest that the Northern Hemisphere during the mid-Holocene (MH), to be assumed herein to correspond to 6,000 years ago, was generally warmer than today during summer and colder in the winter due to the enhanced seasonal contrast in the amount of solar radiation reaching the top of the atmosphere. The complex orography of both India and Southeast Asia (SEA), which includes the Himalayas and the Tibetan Plateau (TP) in the north and the Western Ghats mountains along the west coast of India in the south, renders the regional climate complex and the simulation of the intensity and spatial variability of the MH summer monsoon technically challenging. In order to more accurately capture important regional features of the monsoon system in these regions, we have completed a series of regional climate simulations using a coupled modeling system consisting of the University of Toronto version of the Coupled Climate System Model version 4 (UofT-CCSM4), the Weather Research and Forecasting (WRF) regional climate model and the 3D Coastal and Regional Ocean Community model (CROCO) to dynamically downscale MH global simulations constructed using UofT-CCSM4. In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions in order to compare with standard MH simulations and to capture interactions between the GS and the monsoon circulations in India and SEA. In both the global and the regional models, the response of the South Asia (SA) and SEA monsoons to MH orbital forcing is intensified and accompanies lower surface temperature which is likely related to the increased reflectance of shortwave flux at high levels from the greater cloud cover. Comparison of simulated and reconstructed climates suggest that the dynamically downscaled simulations produce significantly more realistic anomalies in the Asian monsoon than the global climate model, although they both continue to underestimate the inferred changes in precipitation based upon reconstructions using climate proxy information. Monsoon precipitation over SA and SEA is also greatly influenced by the inclusion of a GS, with a large increase in particular being predicted over northern SA and SEA, and a lengthening of the monsoon season. Data-model comparison with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data-model comparison.