Uncertainty in Future Agro-Climate Projections in the United States and Benefits of Greenhouse Gas Mitigation

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivity, and natural variability. By end of century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.This work was partially funded by the US Environmental Protection Agency’s Climate Change Division, under Cooperative Agreement #XA-83600001, by the US Department of Energy, Office of Biological and Environmental Research, under grant DE-FG02-94ER61937, and by the National Science Foundation Macrosystems Biology Program Grant #EF1137306. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. (For the complete list see http://globalchange.mit.edu/sponsors/current.html). This research used the Evergreen computing cluster at the Pacific Northwest National Laboratory. Evergreen is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-76RL01830

Estimating the potential of U.S. urban infrastructure albedo enhancement as climate mitigation in the face of climate variability

The climate mitigation potential of U.S. urban infrastructure albedo enhancement is explored using multidecadal regional climate simulations. Increasing albedo from 0.2 to 0.4 results in summer daytime surface temperature decreases of 1.5°C, substantial reductions in health-related heat (50% decrease in days with danger heat advisory) and decreases in energy demand for air conditioning (15% decrease in cooling degree days) over the U.S. urban areas. No significant impact is found outside urban areas. Most regional modeling studies rely on short simulations; here, we use multidecadal simulations to extract the forced signal from the noise of climate variability. Achieving a ±0.5°C margin of error for the projected impacts of urban albedo enhancement at a 95% confidence level entails using at least 5 simulation years. Finally, single-year higher-resolution simulations, requiring the same computing power as the multidecadal coarser-resolution simulations, add little value other than confirming the overall magnitude of our estimates.This work was supported by the Concrete Sustainability Hub at MIT, with sponsorship provided by the Portland Cement Association and the RMC Research & Education Foundation, and by the US Department of Energy, Office of Biological and Environmental Research, under grant DE-FG02-94ER61937. The MIT Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. For a complete list of sponsors, see http://globalchange.mit.edu

Impact of Canopy Representations on Regional Modeling of Evapotranspiration using the WRF-ACASA Coupled Model

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. Accurate estimates of evapotranspiration (both potential and actual) are especially important for regions with limited water availability and high water demand, such as California. Water availability has been and will continue to be the most important issue facing California for years and perhaps decades to come. Terrestrial evapotranspiration is influenced by many processes and interactions in the atmosphere and the bio-sphere such as water, carbon, and momentum exchanges. The need to improve representation within of surface-atmosphere interactions remains an urgent priority within the modeling community.This work is supported in part by the National Science Foundation under Awards No.ATM-0619139 and EF-1137306. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. (For the complete list see http://globalchange.mit.edu/sponsors/current.html)

Modeling Regional Carbon Dioxide Flux over California using the WRF‑ACASA Coupled Model

Many processes and interactions in the atmosphere and the biosphere influence the rate of carbon dioxide exchange between these two systems. However, it is difficult to estimate the carbon dioxide flux over regions with diverse ecosystems and complex terrains, such as California. Traditional carbon dioxide measurements are sparse and limited to specific ecosystems. Therefore, accurately estimating carbon dioxide flux on a regional scale remains a major challenge. In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF lack the capability to simulate carbon dioxide. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Carbon dioxide, sensible heat, water vapor, and momentum fluxes between the atmosphere and land surface are estimated in the ACASA model through turbulence equations with a third order closure scheme. It therefore permits counter-gradient transports that low-order turbulence closure models are unable to simulate. A new CO2 tracer module is introduced into the model framework to allow the atmospheric carbon dioxide concentration to vary according to terrestrial responses. In addition to the carbon dioxide simulation, the coupled WRF-ACASA model is also used to investigate the interactions of neighboring ecosystems in their response to atmospheric carbon dioxide concentration. The model simulations with and without the CO2 tracer for WRF-ACASA are compared with surface observations from the AmeriFlux network.This work is supported in part by the National Science Foundation under Awards No.ATM-0619139 and EF-1137306. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. (For the complete list see http://globalchange.mit.edu/sponsors/current.html)

The influence of gravitational lensing on the spectra of lensed QSOs

We consider the influence of (milli/micro)lensing on the spectra of lensed QSOs. We propose a method for the observational detection of microlensing in the spectra of lensed QSOs and apply it to the spectra of the three lensed QSOs (PG 1115+080, QSO 1413+117 and QSO 0957+561) observed with Hubble Space Telescope (HST). We find that the flux ratio between images A1 and A2 of PG 1115+080 is wavelength-dependent and shows differential magnification between the emission lines and the continuum. We interpret this magnification as arising from millilensing. We also find that the temporal variations in the continuum of image C of QSO 1413+117 may be caused by microlensing, while the temporal variation observed in QSO 0957+561 was probably an intrinsic one.Comment: 11 pages, accepted for publication in MNRA

Quantum Probabilistic Subroutines and Problems in Number Theory

We present a quantum version of the classical probabilistic algorithms $\grave{a}$ la Rabin. The quantum algorithm is based on the essential use of Grover's operator for the quantum search of a database and of Shor's Fourier transform for extracting the periodicity of a function, and their combined use in the counting algorithm originally introduced by Brassard et al. One of the main features of our quantum probabilistic algorithm is its full unitarity and reversibility, which would make its use possible as part of larger and more complicated networks in quantum computers. As an example of this we describe polynomial time algorithms for studying some important problems in number theory, such as the test of the primality of an integer, the so called 'prime number theorem' and Hardy and Littlewood's conjecture about the asymptotic number of representations of an even integer as a sum of two primes.Comment: 9 pages, RevTex, revised version, accepted for publication on PRA: improvement in use of memory space for quantum primality test algorithm further clarified and typos in the notation correcte