Observed and Predicted Precipitation Changes in Pakistan Using Ground Observations, Satellite Data and Model Projections with Special Focus on Winter and Pre-Monsoon Precipitation

This study utilizes ground, satellite and model data to investigate the observed and future precipitation changes in Pakistan. Pakistan Meteorological Department’s (PMD) monthly precipitation data set along with Tropical Rainfall Measuring Mission (TRMM) monthly dataset TRMM_3B43 (0.25˚x0.25˚ resolution) have been used to evaluate rainfall trends over the climatic zones of Pakistan through Man-Kendall test and Sen’s slope estimator for the time period 1978-2018. Community Climate System Model (CCSM4) projections have been employed to explore the projected changes in precipitation till 2099. Furthermore, TRMM and CCSM4 projections have been correlated and validated using Root Mean Square Error (RMSE) and Mean Bias Error (MBE). There is a good correlation between TRMM and PMD ground observation at all stations of the country for all seasons, with correlation coefficient values ranging from 0.89 (November) to 0.97 (July and August). The study shows a decreasing trend in winter precipitation in all zones of the country with a significant decrease over western mountains i.e. zone C of the country. During 2008-2018, a sharp decrease in winter precipitation is observed as compared to the baseline value of 1978-2007 in all climatic zones. There seems to be a shift in precipitation from winter towards pre-monsoon season as pre-monsoon precipitation in last 11 years increased in all zones except Zone C. Coherently, there is a decrease in area affected by winter precipitation and an increase in area for pre-monsoon precipitation. Future precipitation estimates from CCSM4 model for RCP 4.5 and RCP 8.5 over-estimate precipitation in most parts of the country for the first 9 observed years (2010-2018) and predict a rise in precipitation by 2099 which is more pronounced in the northern and western Pakistan while a decrease is predicted for the plains of the country, which might have negative consequences for agriculture.

Potential distribution of the primary malaria vector Anopheles gambiae Giles [Diptera: Culicidae] in Southwest Nigeria under current and future climatic conditions

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.

Thermal Niche for Seed Germination and Species Distribution Modelling of Swietenia macrophylla King (Mahogany) under Climate Change 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.

Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

Land surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Sea surface temperatures in the South China Sea as a natural thermostat to the rainfall over Borneo: preliminary results

Community Climate System Model version 4 (CCSM4) simulated warmer sea surface temperatures (SSTs) in the South China Sea (SCS) for the mid-Holocene scenario compared to the pre-Industrial. Previous sensitivity experiments using the atmospheric component of the CCSM4, the Community Atmospheric Model version 4 (CAM4), showed that warmer SSTs in the SCS suppresses rainfall over Borneo, which is in-contrary to the effect of the stronger insolation over the island. In this study, we show that warmer SSTs in the SCS, as simulated in the CCSM4, is responding to a weaker low-level wind impacted by the stronger convectional rainfall over Borneo due to stronger insolation. These results suggest that warmer SSTs in the SCS might act as a negative feedback which damps the effect of the stronger insolation on rainfall changes over Borneo.

Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (Pic.-Serm.) (Fabaceae) in Ethiopia

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. & 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.

Effect of horizontal resolution on the simulation of tropical cyclones in the Chinese Academy of Sciences FGOALS-f3 climate system model

The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project phase 6 (CMIP6). Both the low-resolution (about 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (about 25 km resolution) FGOALS-f3 (FGOALS-f3-H) models were used to achieve the standard Tier 1 experiment required by HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with the exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated using observations. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. Although the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, both FGOALS-f3-L and FGOALS-f3-H appear to replicate the timing of the seasonal cycle of tropical cyclones. The simulated average and interannual variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins. The characteristics of tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the response between the El Niño–Southern Oscillation and the number of tropical cyclones and the accumulated cyclone energy in FGOALS-f3 contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of resolution. We discuss the current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the subseasonal to seasonal prediction of tropical cyclones.

Current and future predicting habitat suitability map of Cunninghamia konishii Hayata using MaxEnt model under climate change in Northern Vietnam

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.

Dynamical-Statistical Hybrid Seasonal Prediction of Tropical Cyclone Track Density Over Western North Pacific

Compared with total account of basin-wide tropical cyclones (TC) genesis, the prevailing tracks of TC activity and its potential of landfalling is more important for disaster prevention. Despite its relatively lower predictability, a statistical-dynamical hybrid prediction model was developed based on the knowledge of the physical mechanism between western North Pacific (WNP) TC activity and related large-scale environmental fields from July to September. The leading modes of spatial-temporal variation of WNP TC tracks density its climatological peak season (July to September) was extracted using empirical orthogonal function (EOF) decomposition. The interannual variation of leading EOF modes of WNP TC track density was predicted using multiple linear regressions (MLR) method based on predictors selected by correlation analysis of both observational and Beijing Climate Center climate system model version 1.1 (BCC_CSM1.1) hindcast data. The predicted spatial distribution of WNP TC tracks density was obtained through weighted composite of forecasting EOF modes according to its variance explained respectively. Results of one-year-out cross validation indicates that forecast model well captures the interannual variation of WNP TC prevailing moving tracks, especially in South China Sea (SCS) and southeastern quadrant of WNP. The prediction skill enhanced with decreased forecast lead time, with anomaly correlation coefficient (ACC) of northern SCS and southeast quadrant of WNP reaches 0.6 for the period 1991-2020 with one month forecast lead time. Forecast assessment based on different ENSO phases indicate that source of predictability of WNP TC tracks was mainly originate from ENSO events, especially strong El Niño events.

Improving the Spring Air Temperature Forecast Skills of BCC_CSM1.1 (m) by Spatial Disaggregation and Bias Correction: Importance of Trend Correction

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.