Predicting the timing of cherry blossoms in Washington, DC and Mid-Atlantic States in response to climate change.

Cherry blossoms, an icon of spring, are celebrated in many cultures of the temperate region. For its sensitivity to winter and early spring temperatures, the timing of cherry blossoms is an ideal indicator of the impacts of climate change on tree phenology. Here, we applied a process-based phenology model for temperate deciduous trees to predict peak bloom dates (PBD) of flowering cherry trees (Prunus×yedoensis 'Yoshino' and Prunus serrulata 'Kwanzan') in the Tidal Basin, Washington, DC and the surrounding Mid-Atlantic States in response to climate change. We parameterized the model with observed PBD data from 1991 to 2010. The calibrated model was tested against independent datasets of the past PBD data from 1951 to 1970 in the Tidal Basin and more recent PBD data from other locations (e.g., Seattle, WA). The model performance against these independent data was satisfactory (Yoshino: r(2) = 0.57, RMSE = 6.6 days, bias = 0.9 days and Kwanzan: r(2) = 0.76, RMSE = 5.5 days, bias = -2.0 days). We then applied the model to forecast future PBD for the region using downscaled climate projections based on IPCC's A1B and A2 emissions scenarios. Our results indicate that PBD at the Tidal Basin are likely to be accelerated by an average of five days by 2050 s and 10 days by 2080 s for these cultivars under a mid-range (A1B) emissions scenario projected by ECHAM5 general circulation model. The acceleration is likely to be much greater (13 days for 2050 s and 29 days for 2080s) under a higher (A2) emissions scenario projected by CGCM2 general circulation model. Our results demonstrate the potential impacts of climate change on the timing of cherry blossoms and illustrate the utility of a simple process-based phenology model for developing adaptation strategies to climate change in horticulture, conservation planning, restoration and other related disciplines

Quantifying the impact of weather extremes on global food security: A spatial bio-economic approach

This study uses a spatial bio-economic modelling framework to estimate the impact of the 2012 weather extreme in the USA on food security in the developing world. The study also quantifies the potential effects of a similar weather extreme occurring in 2050 under climate change. The study results indicate that weather extremes that affect maize productivity in key grain baskets can negatively affect food security in vulnerable countries. The 2012 weather extreme which occurred in the USA reduced US and global maize production by 29% compared to trend; maize consumption in the country decreased by 5% only and this resulted in less surplus maize for exports from the largest maize exporter in the world. Global maize production decreased by 6% compared to trend. The decrease in global maize production coupled with a reduction in the volume of global maize exports worsened food insecurity in eastern Africa, the Caribbean and Central America and India. The effects of the weather extreme on global food security would be worse, if the latter were to occur under climate change in 2050, assuming no climate change adaptation worldwide over the years. In addition, the hardest-hit regions would remain the same, whether the weather extreme occurs in 2012 instead of 2050: Sub-Saharan Africa (SSA), South Asia and the Latin America and Caribbean (LAC) region. However, sustained growth in per capita income across world economies between 2000 and 2050 would allow few countries in SSA and the LAC region to virtually eliminate hunger within their borders. In these countries, per capita income would be high enough by 2050 to completely offset the negative effect of the weather extreme. The study results are also consistent with USDA׳s estimates on US and global maize production and consumption in 2012 after the weather extreme. Some discrepancy is found on the volume of global maize trade; this implies that the bio-economic model likely overestimates the effect of the weather extreme on food insecurity. However, the trends from the analysis are likely to be valid. Further research would involve using a CGE model that can capture the net effects of weather extremes

Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world

PRIFPRI3; CRP2PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

Model parameterization results for predicting peak bloom dates (PBD) of Yoshino (A, B) and Kwanzan cherry trees (C, D) in the Tidal Basin, Washington, DC during 1991–2010.

<p>Predicted PBD vs. observed PBD are shown in A (Yoshino) and C (Kwanzan). The temporal trends of PBD for the same period are shown in B (Yoshino) and D (Kwanzan). The observed PBD are represented by open cycles (○) with a solid line and the predicted PBD by cross symbol (×) with a dashed line.</p

Predicted mean peak bloom dates of cherry trees (<i>Prunus</i>×<i>yedoensis</i> ‘Yoshino’ and <i>Prunus serrulata</i> ‘Kwanzan’) at selected Mid-Atlantic locations in response to the projected climate under the A1B and A2 emission scenarios of MPI-ECHAM5 and CCCMA-CGCM2 general circulation models, respectively, during 2010–2099.

<p>The observed mean peak bloom dates during 1971–2000 at the Tidal Basin are April 2 for Yoshino and April 15 for Kwanzan during a time period under each sc. SD represents one standard deviation across different time periods within a scenario at each location, or across the four locations.</p