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.

Dynamical Downscaling of Temperature Variations over the Canadian Prairie Provinces under Climate Change

In this study, variations of daily mean, maximum, and minimum temperature (expressed as Tmean, Tmax, and Tmin) over the Canadian Prairie Provinces were dynamically downscaled through regional climate simulations. How the regional climate would increase in response to global warming was subsequently revealed. Specifically, the Regional Climatic Model (RegCM) was undertaken to downscale the boundary conditions of Geophysical Fluid Dynamics Laboratory Earth System Model Version 2M (GFDL-ESM2M) over the Prairie Provinces. Daily temperatures (i.e., Tmean, Tmax, and Tmin) were subsequently extracted from the historical and future climate simulations. Temperature variations in the two future periods (i.e., 2036 to 2065 and 2065 to 2095) are then investigated relative to the baseline period (i.e., 1985 to 2004). The spatial distributions of temperatures were analyzed to reveal the regional impacts of global warming on the provinces. The results indicated that the projected changes in the annual averages of daily temperatures would be amplified from the southwest in the Rocky Mountain area to the northeast in the prairie region. It was also suggested that the projected temperature averages would be significantly intensified under RCP8.5. The projected temperature variations could provide scientific bases for adaptation and mitigation initiatives on multiple sectors, such as agriculture and economic sectors over the Canadian Prairies.

Long-Term Projection of Water Cycle Changes over China Using RegCM

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.

A Comparison of Tropical Cyclone Projections in a High-resolution Global Climate Model and from Downscaling by Statistical and Statistical-deterministic Methods

In this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Forecast-oriented Low Ocean Resolution (HiFLOR) model, under the Representative Concentration Pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic basin. The downscaled TCs for the historical climate (1986-2005) are compared with those in the mid- (2016-35) and late-twenty-first century (2081-2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that while significantly more storms are detected in HiFLOR towards the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency, and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and Category 5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity while PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity.

On the sensitivity of hurricane intensity and structure to horizontal tracer advection schemes in FV3

We investigate the sensitivity of hurricane intensity and structure to the horizontal tracer advection in the Geophysical Fluid Dynamics Laboratory (GFDL) Finite-Volume Cubed-Sphere Dynamical Core (FV3). We compare two schemes, a monotonic scheme and a less diffusive positive-definite scheme. The positive-definite scheme leads to significant improvement in the intensity prediction relative to the monotonic scheme in a suite of five-day forecasts that mostly consist of rapidly intensifying hurricanes. Notable storm structural differences are present: the radius of maximum wind (RMW) is smaller and eyewall convection occurs farther inside the RMW when the positive-definite scheme is used. Moreover, we find that the horizontal tracer advection scheme affects the eyewall convection location by affecting the moisture distribution in the inner-core region. This study highlights the importance of dynamical core algorithms in hurricane intensity prediction.

Important factors in the tracking of tropical cyclones in operational models

Multiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

Seasonal prediction and predictability of regional Antarctic sea ice

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

Predictive distribution modeling of Swertia bimaculata in Darjeeling-Sikkim Eastern Himalaya using MaxEnt: current and future scenarios

Background As global temperatures continue to rise, species distribution modeling is a suitable tool for identifying rare and endangered species most at risk of extinction, along with tracking shifting geographical range. Methods The present study investigates the potential distribution of Swertia bimaculata in the Darjeeling-Sikkim region of Eastern Himalaya in current and future climate scenarios of GFDL-CM3 (Geophysical Fluid Dynamics Laboratory-Climate Model 3) for the year 2050 and year 2070 through MaxEnt presence data modeling. Two sets of variables were used for modeling current scenario. The models were evaluated using AUC (area under the curve) values and TSS (true skill statistic). Results Habitat assessment of the species shows low and sporadic distribution within the study area. A significant decrease is observed in the possible range of the species in the future climate scenario with the habitat decreasing from 869.48 to 0 km2. Resultant maps from the modeling process show significant upward shifting of the species range along the altitudinal gradient. Still, results should be taken with caution given the low number of occurrences used in the modeling. Conclusions The results thus highlight the vulnerability of the species towards extinction in the near future.

Representation of Tropical Mesoscale Convective Systems in a General Circulation Model: Climatology and Response to Global Warming

The characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50-km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures (SST) and sea-ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean.

Skillful seasonal prediction of North American summer hot days

<p>This study shows that the frequency of North American summer hot days are skillfully predictable months in advance in the newly-developed GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless System for Prediction and EArth System Research) seasonal forecast system. We also demonstrate that climate change, the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO) and atmosphere-land feedback all contribute to the seasonal predictive skill of the frequency of North American summer hot days. Using a statistical optimization method (Average Predictability Time) we identify two large-scale components of the frequency of North American summer hot days that are predictable with significant correlation skill. One component shows an increase in the frequency of summer hot days everywhere over North America and is highly predictable at least 9 months in advance. This component is related to a secular warming trend. Another predictable component shows largest loadings over the central U.S., and is significantly predictable 6 months ahead. This second component is related to the PDO and the AMO, and is significantly correlated with the central U.S. soil water. These findings have potential implications for predictions of North American summer hot days on seasonal time scales.</p>