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BACKGROUND
Anticipating changes in international migration patterns is useful for demographic studies and for designing policies that support the well-being of those involved. Existing forecasting methods do not account for a number of stylized facts that emerge from large-scale migration observations and theories: existing migrant communities - diasporas - act to lower migration costs and thereby provide a mechanism of self-amplification; return migration and transit migration are important components of global migration flows; and poverty constrains emigration.
OBJECTIVE
Here we present hindcasts and future projections of international migration that explicitly account for these nonlinear features.
METHODS
We develop a dynamic model that simulates migration flows by origin, destination, and place of birth. We calibrate the model using recently constructed global datasets of bilateral migration.
RESULTS
We show that the model reproduces past patterns and trends well based only on initial migrant stocks and changes in national incomes. We then project migration flows under future scenarios of global socioeconomic development.
CONCLUSIONS
Different assumptions about income levels and between-country inequality lead to markedly different migration trajectories, with migration flows either converging towards net zero if incomes in presently poor countries catch up with the rest of the world; or remaining high or even rising throughout the 21st century if economic development is slower and more unequal. Importantly, diasporas induce significant inertia and sizable return migration flows.
Human migration is often studied using gravity models. These models, however, have known limitations, including analytic inconsistencies and a dependence on empirical data to calibrate multiple parameters for the region of interest. Overcoming these limitations, the radiation model has been proposed as an alternative, universal approach to predicting different forms of human mobility, but has not been adopted for studying migration. Here we show, using data on within-country migration from the USA and Mexico, that the radiation model systematically underpredicts long-range moves, while the traditional gravity model performs well for large distances. The universal opportunity model, an extension of the radiation model, shows an improved fit of long-range moves compared to the original radiation model, but at the cost of introducing two additional parameters. We propose a more parsimonious extension of the radiation model that introduces a single parameter. We demonstrate that it fits the data over the full distance spectrum and also-unlike the universal opportunity model-preserves the analytical property of the original radiation model of being equivalent to a gravity model in the limit of a uniform population distribution.
Non-linear intensification of Sahel rainfall as a possible dynamic response to future warming
(2017)
Abrupt monsoon transitions as seen in paleorecords can be explained by moisture-advection feedback
(2016)
We present climatic consequences of the Representative Concentration Pathways (RCPs) using the coupled climate model CLIMBER-3 alpha, which contains a statistical-dynamical atmosphere and a three-dimensional ocean model. We compare those with emulations of 19 state-of-the-art atmosphere-ocean general circulation models (AOGCM) using MAGICC6. The RCPs are designed as standard scenarios for the forthcoming IPCC Fifth Assessment Report to span the full range of future greenhouse gas (GHG) concentrations pathways currently discussed. The lowest of the RCP scenarios, RCP3-PD, is projected in CLIMBER-3 alpha to imply a maximal warming by the middle of the 21st century slightly above 1.5 degrees C and a slow decline of temperatures thereafter, approaching today's level by 2500. We identify two mechanisms that slow down global cooling after GHG concentrations peak: The known inertia induced by mixing-related oceanic heat uptake; and a change in oceanic convection that enhances ocean heat loss in high latitudes, reducing the surface cooling rate by almost 50%. Steric sea level rise under the RCP3-PD scenario continues for 200 years after the peak in surface air temperatures, stabilizing around 2250 at 30 cm. This contrasts with around 1.3 m of steric sea level rise by 2250, and 2 m by 2500, under the highest scenario, RCP8.5. Maximum oceanic warming at intermediate depth (300-800 m) is found to exceed that of the sea surface by the second half of the 21st century under RCP3-PD. This intermediate-depth warming persists for centuries even after surface temperatures have returned to present-day values, with potential consequences for marine ecosystems, oceanic methane hydrates, and ice-shelf stability. Due to an enhanced land-ocean temperature contrast, all scenarios yield an intensification of monsoon rainfall under global warming.
Indian monsoon rainfall is vital for a large share of the world's population. Both reliably projecting India's future precipitation and unraveling abrupt cessations of monsoon rainfall found in paleorecords require improved understanding of its stability properties. While details of monsoon circulations and the associated rainfall are complex, full-season failure is dominated by large-scale positive feedbacks within the region. Here we find that in a comprehensive climate model, monsoon failure is possible but very rare under pre-industrial conditions, while under future warming it becomes much more frequent. We identify the fundamental intraseasonal feedbacks that are responsible for monsoon failure in the climate model, relate these to observational data, and build a statistically predictive model for such failure. This model provides a simple dynamical explanation for future changes in the frequency distribution of seasonal mean all-Indian rainfall. Forced only by global mean temperature and the strength of the Pacific Walker circulation in spring, it reproduces the trend as well as the multidecadal variability in the mean and skewness of the distribution, as found in the climate model. The approach offers an alternative perspective on large-scale monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions.
The possibility of an impact of global warming on the Indian monsoon is of critical importance for the large population of this region. Future projections within the Coupled Model Intercomparison Project Phase 3 (CMIP-3) showed a wide range of trends with varying magnitude and sign across models. Here the Indian summer monsoon rainfall is evaluated in 20 CMIP-5 models for the period 1850 to 2100. In the new generation of climate models, a consistent increase in seasonal mean rainfall during the summer monsoon periods arises. All models simulate stronger seasonal mean rainfall in the future compared to the historic period under the strongest warming scenario RCP-8.5. Increase in seasonal mean rainfall is the largest for the RCP-8.5 scenario compared to other RCPs. Most of the models show a northward shift in monsoon circulation by the end of the 21st century compared to the historic period under the RCP-8.5 scenario. The interannual variability of the Indian monsoon rainfall also shows a consistent positive trend under unabated global warming. Since both the long-term increase in monsoon rainfall as well as the increase in interannual variability in the future is robust across a wide range of models, some confidence can be attributed to these projected trends.
The Indian summer monsoon shapes the livelihood of a large share of the world's population. About 80% of annual precipitation over India occurs during the monsoon season from June through September. Next to its seasonal mean rainfall, the day-to-day variability is crucial for the risk of flooding, national water supply, and agricultural productivity. Here we show that the latest ensemble of climate model simulations, prepared for the AR-5 of the Intergovernmental Panel on Climate Change, consistently projects significant increases in day-to-day rainfall variability under unmitigated climate change. The relative increase by the period 2071-2100 with respect to the control period 1871-1900 ranges from 13% to 50% under the strongest scenario (Representative Concentration Pathways, RCP-8.5), in the 10 models with the most realistic monsoon climatology; and 13% to 85% when all the 20 models are considered. The spread across models reduces when variability increase per degree of global warming is considered, which is independent of the scenario in most models, and is 8% +/- 4%/K on average. This consistent projection across 20 comprehensive climate models provides confidence in the results and suggests the necessity of profound adaptation measures in the case of unmitigated climate change.