Hindcasting

While it may be tempting to quickly move on from our failings and shortcomings, given the seriousness of potential consequences within the fields of Homeland Security and Emergency Management, having a method of practice and process to efficiently and effectively review and dissect past incidents and events can be useful to formulating future planning and strategy. Hindcasting, which is also sometimes referred to as backtesting, may be the perfect vehicle for just such introspection.

in the periods of 550-650 and 1550-1650 CE was carried out to test the efficacy of Artificial Neural Network (ANN) to predict past environments for Archaeology. This work shows that both subtropical Yunga and xerophytic Chaqueña vegetations respond in contrasting fashion to changes in climate forcings. To predict the past an ANN perceptron multilayer model was used. Modern NDVI data and Tree-Ring data were obtained from NOAA-Paleoclimate, and other public sources. These data were used to train the model. Real data and predictions were close (Pearson correlation 0.83-0.90) and warranted the following step, hindcasting. Important paleo-NDVI fluctuations lasting 15 to 20 years were identified in both periods under study. The paleo-NDVI fluctuations in the earlier period were probably related to the unidentified eruption of 583. The fluctuations in the later period appear related to the eruption of 1600 of the Huaynaputina volcano (SW Peru). These findings suggest that the model accurately identified vegetation fluctuations in response to changes in the volcanic forcing. Hence, the ANNs may be considered as apt tools for modeling past environments in support of archaeology.

The aim of this paper is to test the ability of neural network approaches to hindcast the spring standardized precipitation index on a 6-month time scale (SPI6) in Portugal, based on winter large-scale climatic indices. For this purpose, the linkage of the spring SPI time series with the winter North Atlantic Oscillation (NAO) and the sea surface temperature (SST) was investigated by means of maps of the correlation coefficient for the period from October 1910 to September 2004. The results indicate that the winter NAO is a good predictor for the SPI6 of the spring (SPI6 finishing in April, May and June, SPI6 April , SPI6 May and SPI6 June , respectively) for the northern, central and southern regions of Portugal. The winter SST1 (area of the Mediterranean Sea) must only be considered for the northern region, and the winter SST3 (area of the North Atlantic between Iberia and North America) only for the southern region. Spatial maps of predictive SPI6 for April, May and June were created and validated. The neural models explained more than 81% of the total variance for the SPI6 April and SPI6 May and more than 64% of the total variance for the SPI6 June . Probability maps were also developed considering the values predicted by the neural methods for the spring months and all drought categories (moderate, severe and extreme). These maps indicating the probability of droughts can provide valuable support for the integrated planning and management of water resources throughout Portugal.

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in , this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

Aim Climate changes are thought to be responsible for the retreat and eventual extinction of subtropical lauroid species that covered much of Europe and North Africa during the Palaeogene and early Neogene; little is known, however, of the spatial and temporal patterns of this demise. Herein we calibrate ecological niche models to assess the climatic requirements of Laurus L. (Lauraceae), an emblematic relic from the Tethyan subtropical flora, subsequently using these models to infer how the range dynamics of Laurus were affected by Plio-Pleistocene climate changes. We also provide predictions of likely range changes resulting from future climatic scenarios.

Ongoing rapid climate change is predicted to cause local extinction of plant species in mountain regions. However, some plant species could have persisted during Quaternary climate oscillations without shifting their range, despite the limited evidence from fossils. Here, we tested two candidate mechanisms of persistence by comparing the macrorefugia and microrefugia (MR) hypotheses. We used the rare and endemic Saxifraga florulenta as a model taxon and combined ensembles of species distribution models (SDMs) with a high-resolution paleoclimatic and topographic dataset to reconstruct its potential current and past distribution since the last glacial maximum. To test the macrorefugia hypothesis, we verified whether the species could have persisted in or shifted to geographic areas defined by its realized niche. We then identified potential MR based on climatic and topographic properties of the landscape and applied refined scenarios of MR dynamics and functions over time. Last, we quantified the number of known occurrences that could be explained by either the macrorefugia or MR model. A consensus of two or three SDM techniques predicted absence between 14-10, 3-4 and 1 ka BP, which did not support the macrorefugia model. In contrast, we showed that S. florulenta could have contracted into MR during periods of absence predicted by the SDMs and later re-colonized suitable areas according to the macrorefugia model. Assuming a limited and realistic seed dispersal distance for our species, we explained a large number of the current occurrences (61-96%). Additionally, we showed that MR could have facilitated range expansions or shifts of S. florulenta. Finally, we found that the most recent and the most stable MR were the ones closest to current occurrences. Hence, we propose a novel paradigm to explain plant persistence by highlighting the importance of supporting functions of MR when forecasting the fate of plant species under climate change.

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

Paleoclimatic reconstructions coupled with species distribution models and identification of extant spatial genetic structure have the potential to provide insights into the demographic events that shape the distribution of intra-specific genetic variation across time. Using the globeflower Trollius europaeus as a case-study, we combined (1) Amplified Fragment Length Polymorphisms, (2) suites of 1000-years stepwise hindcasted species distributions and (3) a model of diffusion through time over the last 24,000 years, to trace the spatial dynamics that most likely fits the speciesÕ current genetic structure. We show that the globeflower comprises four gene pools in Europe which, from the dry period preceding the Last Glacial Maximum, dispersed while tracking the conditions fitting its climatic niche. Among these four gene pools, two are predicted to experience drastic range retraction in the near future. Our interdisciplinary approach, applicable to virtually any taxon, is an advance in inferring how climate change impacts speciesÕ genetic structures.

Abstract Climate on earth has always been changing. Despite decades of investigation, our limited knowledge of the ecological and evolutionary effects of climate changes often translates into uncertain predictions about the impact of future climates on biodiversity. Integrative biogeographical approaches using palaeobotanical, phylogenetic and niche-based species distribution models, when permitted by data availability, may provide valuable insights to address these key questions. Here we combine palaeobotanical and ...

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81-2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R 2 = 0.783, P < 0.001). Based on NMDD parameters, current lake ice conditions were characterized in four ways: heavy, moderate, light and very light. Lake Ontario ice conditions were reconstructed to create a hindcast for the span of the instrumental temperature record (1840/41-1979/80). Based on a decadal analysis, heavy ice seasons decreased significantly (R 2 = 0.658, P < 0.001) from the 1840s to the 2000s, declining from an average of 6 heavy ice seasons per decade during the most distant climate normal (1840s to 1960s) to an average of only 1 heavy ice season per decade during the most recent climate normal (1980s to 2000s). Finally, lake ice conditions are projected to the end of the 21st century, using an optimal ensemble of Global Climate Model outputs for two different climate change scenarios (RCP4.5, RCP8.5). Heavy ice seasons no longer occur as early as the 2050s under both RCP4.5 and RCP8.5. Whereas, very light ice seasons go from being an extreme in the baseline period (10%), to the dominant characterization of Lake Ontario ice conditions by the 2080s, for both RCP4.5 (73%) and RCP8.5 (100%).

The forests in the Iberian Peninsula have been strongly influenced by past climatic changes, but reconstructing their historical distributions and dynamics is very difficult due to the complex climatic characteristics and relief of the region. Research disciplines such as phylogeography and species distribution modelling can describe the past range dynamics of individual tree species in relatively great detail and help elucidate how these species have reacted to climatic changes. Here we review phylogeographical and modelling studies from species representative of the major Iberian forest types and attempt to extract general trends from the diversity of individual species histories in the Peninsula. To date most studies focus on geographical range dynamics during the Pleistocene, but an increasing body of evidence shows that some species have also retained the genetic imprints of much more ancient processes. Many widespread Iberian species show a deep and often remarkably clear-cut divide between populations from the Mediterranean and from the Atlantic regions of the Iberian Peninsula, suggesting that both areas have independently sustained viable populations over extended periods. In fact, phylogeographical studies commonly find that species had several glacial refugia across the Iberian Peninsula. On the other hand, distribution models help identifying further suitable areas that could have sustained so far undetected refugia. Such studies are of interest for species conservation, because refugium populations are high-priority targets due to their longterm persistence and unique evolutionary trajectory. Overall, we conclude that palaeoecology, phylogeography and species distribution modelling have a great potential to inform each other because of their complementary perspectives and results. A true integration of these approaches is therefore fundamental for further progress in our understanding of past Iberian environments and the organisms they harboured.

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid-Holocene climate to test the ability of SDMs to predict past climate-change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among-species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns.

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in , this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

The aim of this paper is to test the ability of neural network approaches to hindcast the spring standardized precipitation index on a 6-month time scale (SPI6) in Portugal, based on winter large-scale climatic indices. For this purpose, the linkage of the spring SPI time series with the winter North Atlantic Oscillation (NAO) and the sea surface temperature (SST) was investigated by means of maps of the correlation coefficient for the period from October 1910 to September 2004. The results indicate that the winter NAO is a good predictor for the SPI6 of the spring (SPI6 finishing in April, May and June, SPI6 April , SPI6 May and SPI6 June , respectively) for the northern, central and southern regions of Portugal. The winter SST1 (area of the Mediterranean Sea) must only be considered for the northern region, and the winter SST3 (area of the North Atlantic between Iberia and North America) only for the southern region. Spatial maps of predictive SPI6 for April, May and June were created and validated. The neural models explained more than 81% of the total variance for the SPI6 April and SPI6 May and more than 64% of the total variance for the SPI6 June . Probability maps were also developed considering the values predicted by the neural methods for the spring months and all drought categories (moderate, severe and extreme). These maps indicating the probability of droughts can provide valuable support for the integrated planning and management of water resources throughout Portugal.

The Prairie Pothole Region (PPR) of North America is a globally important resource that provides abundant and valuable ecosystem goods and services in the form of biodiversity, groundwater recharge, water purification, flood attenuation, and water and forage for agriculture. Numerous studies have found these wetlands, which number in the millions, to be highly sensitive to climate variability. Here, we compare wetland conditions between two 30-year periods (1946-1975; 1976-2005) using a hindcast simulation approach to determine if recent climate warming in the region has already resulted in changes in wetland condition. Simulations using the WETLANDSCAPE model show that 20th century climate change may have been sufficient to have a significant impact on wetland cover cycling. Modeled wetlands in the PPR's western Canadian prairies show the most dramatic effects: a recent trend toward shorter hydroperiods and less dynamic vegetation cycles, which already may have reduced the productivity of hundreds of wetland-dependent species. ), but they provide other important ecosystem goods and services including flood retention, groundwater recharge, water purification, recreation, agriculture, and regional biodiversity (Galatowitsch and van der Valk