The isotopic composition of water in the atmosphere is influenced by how the water evaporated, how it was transported, and how it formed in the cloud before falling. Because these processes are temperature dependent, the isotopic ratios stored in glacial ice and other proxy sources have been used as an indicator of pre-instrumental climate. There is uncertainty, however, as to whether isotopic ratios should be interpreted as a proxy of local temperature, or as a broader indicator of changes in how the vapor was transported. To better understand these processes, the NASA GISS general circulation model (GCM) was used to examine two different types of controls on the isotopic composition of moisture. The first control was the large-scale circulation of the atmosphere. Over Europe, it was found that δ18O is strongly controlled by a Northern Annular Mode-like pattern, detected in both the GCM and for Europe’s high-quality precipitation δ18O data. Over the southwest Yukon, it was found that higher δ18O was associated with moisture transport from the south, which led to a re-interpretation of the large mid-19th century δ18O shift seen in the ice cores from Mt. Logan. The second type of control was microphysical, relating to the way precipitation interacts with vapor after it has formed. Using a GCM sensitivity experiment, the effects of ‘post-condensation exchange’ were found to depend primarily on the proportion between the amount of upstream precipitation that fell as rain and the amount that fell as snow, and at low latitudes, on the strength of atmospheric moisture recycling. This led to a partitioning of the well-observed correlation between temperature and precipitation δ18O into its initial and post-condensation components, and a GCM-based interpretation of satellite measurements of the isotopic composition of water vapor in the troposphere.

Atmospheric rivers (ARs) are important contributors to cool season precipitation in the Southwestern US, and in some cases can lead to extreme hydrometeorological events in the region. We performed a climatological analysis and identified two predominant types of ARs that affect the central mountainous region in Arizona: Type 1 ARs originate in the tropics near Hawaii (central Pacific) and enhance their moisture in the midlatitudes, with maximum moisture transport over the ocean at low-levels of the troposphere. On the other hand, moisture in Type 2 ARs has a more direct tropical origin and meridional orientation with maximum moisture transfer at mid-levels. We then analyze future projections of Southwest ARs in a suite of global and regional climate models used in the North American Regional Climate Change Assessment Program (NARCCAP), to evaluate projected future changes in the frequency and intensity of ARs under warmer global climate conditions. We find a consistent and clear intensification of the water vapor transport associated with the ARs that impinge upon Arizona and adjacent regions, however, the response of AR-related precipitation intensity to increased moisture flux and column-integrated water vapor is weak and no robust variations are projected either by the global or the regional NARCCAP models. To evaluate the effect of horizontal resolution and improve our physical understanding of these results, we numerically simulated a historical AR event using the Weather Research and Forecasting (WRF) model at a 3-km resolution. We then performed a pseudo-global warming experiment by modifying the lateral and lower boundary conditions to reflect possible changes in future ARs (as projected by the ensemble of global model simulations used for NARCCAP). Interestingly we find that despite higher specific humidity, some regions still receive less rainfall in the warming climate experiments - partially due to changes in thermodynamics, but primarily due to AR dynamics. Therefore, we conclude from this analysis that overall future increase in atmospheric temperature and water content as projected by global climate models will not necessarily translate into generalized heavier AR-related precipitation in the Southwestern US.

Regional climate models are commonly used for downscaling global climate simulations to the regional scale using nested limited-area models. One of the main goals of this work was the application of regional model RegCM in very high resolution for the region with complex topography in the framework of EC FP6 project CECILIA. RegCM was employed to downscale climate change scenario simulations performed by ECHAM5 model according to the IPCC A1B emission scenario for Central and Eastern Europe in 10km resolution. Validation of model performance, assessed by nesting RegCM in ERA-40 reanalysis, shows improvement of regional climate patterns mainly in mountainous areas. Temperature is well represented with mostly cold bias around -1 žC. Precipitation is affected by large biases around 80 %, in mountainous areas up to 400 % overestimation in winter. Downscaled climate change signal shows average warming 0.5­1.5 žC in period 2021­2050 and 2­4 žC in period 2071­2100. Precipitation changes are mostly within ±0.5 mm/day. RegCM3­beta version with adjusted precipitation scheme parameters shows improvement of the precipitation bias, difference in climate change is rather negligible. Experiments with different convection schemes of RegCM in a case study for Africa performed in the framework of CORDEX project are...

abstract: This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia. / Dissertation/Thesis / Ph.D. Aerospace Engineering 2012

À l’échelle d’un terroir viticole, le climat présente des variations significatives et joue un rôle important sur les caractéristiques des vins produits. L’adaptation de la filière viticole au changement climatique en cours nécessite la connaissance de l’évolution du climat à l’échelle locale. Cette étude vise à intégrer cette échelle dans les projections climatiques en se basant sur l’utilisation combinée de modèles dynamiques et géostatistiques. Dans un premier temps, l’utilisation d’un modèle climatique régional à haute résolution (1 km) dans les vignobles de Marlborough (Nouvelle-Zélande) a permis de cartographier les températures d’une région viticole. Les limites et les incertitudes de l’utilisation de ce type de modèle, notamment pour la représentation des variations thermiques les plus locales, ont également été étudiées. Par l’utilisation des données issues d’un réseau dense de capteurs de température, une seconde étape a consisté au développement d’un modèle statistique non linéaire permettant une cartographie fine des températures sur les appellations Saint-Émilion, Pomerol et leurs satellites. Enfin une méthode d’intégration de l’échelle locale dans les projections de changement climatique est proposée, associant modèles dynamiques et modèles géostatistiques. Cette thèse a mis en évidence que l’utilisation simultanée de différentes méthodes de modélisation des températures peut représenter une piste intéressante pour pallier aux manques qu’elles peuvent représenter individuellement et limiter ainsi l’incertitude. / At vineyard scale, climate variability can be significant in magnitude and play a key role in vine and wine characteristics. Adaptation of viticulture to climate change requires knowledge about future fine-scale climate evolution. This study aims to integrate local scale in future climate projections, coupling dynamic and statistical modelling. A first step consisted in producing temperature maps at 1 km resolution using WRF in a vineyard area (Marlborough, New-Zealand) and evaluating model uncertainties. It revealed that dynamical models do not represent well local climate variations. Using a high density temperature data logger network, the second part is dedicated to developing a non-linear statistical model to map temperature at very fine scale in famous sub-appellations of the Bordeaux vineyard area (Saint-Émilion). Following, a method, coupling dynamical and statistical modelling, is proposed to integrate local scale in climate change projections. This thesis highlights that using simultaneously statistical and dynamical models can be an approach to reduce model uncertainties.

Understanding changes in the burden and growth rate of atmospheric methane (CH4) has been the focus of several recent studies but still lacks scientific consensus. Here we investigate the role of decreasing anthropogenic carbon monoxide (CO) emissions since 2002 on hydroxyl radical (OH) sinks and tropospheric CH4 loss. We quantify this impact by contrasting two model simulations for 2002-2013: (1) a Measurement of the Pollution in the Troposphere (MOPITT) CO reanalysis and (2) a Control-Run without CO assimilation. These simulations are performed with the Community Atmosphere Model with Chemistry of the Community Earth System Model fully coupled chemistry climate model with prescribed CH4 surface concentrations. The assimilation of MOPITT observations constrains the global CO burden, which significantly decreased over this period by similar to 20%. We find that this decrease results to (a) increase in CO chemical production, (b) higher CH4 oxidation by OH, and (c) similar to 8% shorter CH4 lifetime. We elucidate this coupling by a surrogate mechanism for CO-OH-CH4 that is quantified from the full chemistry simulations.

Weather and climate models applied with sufficiently fine mesh grids to enable a large part of atmospheric deep convection to be explicitly resolved have shown a significantly improved representation of local, short-duration and intense precipitation events compared to coarser scale models. In this thesis, two studies are presented aimed at exploring the dependence of horizontal resolution and of parameterization of convection on the simulation of precipitation. The first examined the ability of HARMONIE Climate (HCLIM) regional climate model to reproduce the recent climate in Europe with two different horizontal resolutions, 15 and 6.25 km. The latter is part of the ”grey-zone” resolution interval corresponding to approximately 3-10 km. Particular focus has been given to rainfall and its spatial and temporal variability and other characteristics, for example intensity distributions. The model configuration with the higher resolution is much better at simulating days of large accumulated precipitation amounts, most evident when the comparison is made against high-resolution observations. Otherwise, the two simulations show similar skill, including the representation of the spatial structure of individual rainfall areas of primarily convective origin. The results suggest a ”scale-awareness” in HCLIM, which supports a central feature of the model’s description of deep convection as it is designed to operate independently of the horizontal resolution. In the second study, summer season precipitation over the Alps region, as simulated by HCLIM at different resolutions, is investigated. Similar model configurations as in the previous study were used, but in addition a simulation at the ”convection-permitting” 2 km resolution has been made over Central Europe. The latter considerably increases the realism compared to the former regarding the distribution and intensities of precipitation, as well as other important characteristics including the duration of rain spells, particularly on sub-daily time scales and for extreme events. The simulations with cumulus parameterization active underestimate short-duration heavy rainfall, and rainspells with low peak intensities are too persistent. Furthermore, even though the 6.25 km simulation generally reduces the biases seen in the 15 km run, definitive conclusions of the benefit of ”grey-zone” resolution is difficult to establish in context of the increased requirement of computer resources for the higher-resolution simulation.

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from theWeather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

No description available.

<p></p><p></p><p>Peatlands are the most efficient natural carbon sink on the planet. They are the most carbon-intensive storages than any other vegetation types. However, recent studies indicate that global peatlands can potentially release 6% of the global soil carbon into the atmosphere when they are drained or deforested. They cover only about 3% of the total global land area, but sequester over 30% of the Earth’s soil organic carbon. Peatlands in northern mid-to-high latitudes (45°-90°N) occupy ~90% of the global peatland area and account for ~80% of the total global peat organic carbon stock. Those peatlands are mainly located in Canada, Russia, and the USA. Peatlands in tropical regions cover ~10% of the global peatlands area and store 15-19% of the global peat organic carbon. They are mainly distributed in Southeast Asia and South and Central America. The temperature at the global scale has been rising since the middle of the last century and has accelerated during the last 40 years and the warming will continue in this century. The large storage of soil organic carbon within the peatlands can significantly respond to the changing climate by varying the roles between their carbon sink (from atmosphere to soil) and source (from soil to atmosphere) activities. This dissertation focuses on quantifying the soil organic carbon dynamics in North America and South America using mechanistically-based biogeochemistry models. </p><p></p><p>Peatlands in Alaska occupy 40 million hectares and account for ~10% of the total peatland area in northern mid-to-high latitudes. The regional soil organic carbon dynamics and its response to climate are still with large uncertainty. Most of the studies on peatlands to date are based on short-term site-level observation. This dissertation first used an integrated modeling framework that coupled the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long-term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes and methane emissions were evaluated using long-term observation data at several peatland sites in Minnesota, Alaska, and Canada. The model was then applied for a 10,000-year (15 ka to 5 ka; 1 ka = 1000 cal yr before present) simulation at four peatland sites. The model simulations matched the observed carbon accumulation rates at fen sites during the Holocene (R^2= 0.88, 0.87, 0.38 and -0.05 for four sites respectively using comparisons in 500-year bins from 15 ka to 5 ka). The simulated (2.04 m) and observed peat depths (on average 1.98 m) also compared well (R^2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g 〖C m〗^(-2) yr^(-1)), were estimated up to 6-times higher than the rest of the Holocene (6.5 g 〖C m〗^(-2) yr^(-1)). It suggested that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter-than-before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. The sensitivity tests indicated that, apart from climate, initial water-table depth and vegetation canopy were major drivers to the estimated peat carbon accumulation. </p><p></p><p>To further quantify the regional long-term soil organic carbon accumulation rates and the current carbon stocks in Alaska, the second part of my research focused on quantifying the soil organic carbon accumulation in multiple Alaskan terrestrial ecosystems over the last 15,000 years for both peatland and non-peatland ecosystems. Comparable with the previous estimates of 25-70 Pg carbon (C) in peatlands and 13-22 Pg C in non-peatland soils within 1-m depth in Alaska using peat core data, our model estimated a total SOC of 36-63 Pg C at present, including 27-48 Pg C in peatland soils and 9-15 Pg C in non-peatland soils. Current living vegetation stored 2.5-3.7 Pg C in Alaska with 0.3-0.6 Pg C in peatlands and 2.2-3.1 Pg C in non-peatlands. The simulated average rate of peat soil C accumulation was 2.3 Tg C yr^(-1) with a peak value of 5.1 Tg C yr^(-1) during the Holocene Thermal Maximum (HTM) in the early Holocene, four folds higher than the average rate of 1.4 Tg C yr^(-1) over the rest of the Holocene. The accumulation slowed down, or even ceased, during the neo-glacial climate cooling after the mid-Holocene, but increased again in the 20th century. The model-estimated peat depths ranged from 1.1 to 2.7 m, similar to the field-based estimate of 2.29 m for the region. The changes in vegetation and their distributions were the main factors to determine the spatial variations of SOC accumulation during different time periods. Warmer summer temperature and stronger radiation seasonality, along with higher precipitation in the HTM and the 20th century might have resulted in the extensive peatland expansion and carbon accumulation. </p><p>Most studies on the role of tropical peatlands have focused on Indonesian peatlands. Few have focused on the Amazon basin, where peatlands remain intact and have been a long-term carbon sink. To address the problem, my third study quantified the carbon accumulation for peatland and non-peatland ecosystems in the Pastaza-Marañon foreland basin (PMFB), the most extensive peatland complex in the Amazon basin from 12,000 years before present to 2100 AD. Model simulations indicated that warming accelerated peat carbon loss while increasing precipitation accelerated peat carbon accumulation at millennial time scales. The uncertain parameters and spatial variation of climate were significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, the warming effect on increasing peat carbon loss might overwhelm the wetter effect on increasing peat carbon accumulation. Peat soil carbon accumulation rate in the PMFB slowed down to 7.9 (4.3~12.2) g C m^(-2) yr^(-1) from the current rate of 16.1 (9.1~23.7) g C m^(-2) yr^(-1) and the region might turn into a carbon source to the atmosphere at -53.3 (-66.8~-41.2) g C m^(-2) yr^(-1) (negative indicates source), depending on the level of warming. Peatland ecosystems showed a higher vulnerability than non-peatland ecosystems as indicated by the ratio of their soil carbon density changes (change of soil carbon/existing soil carbon stock) ranging from 3.9 to 5.8). This was primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with non-peatland ecosystems under future climate conditions. Peatland and non-peatland soils in the PMFB might lose up to 0.4 (0.32~0.52) Pg C by 2100 AD with the largest loss from palm swamp. The carbon-dense Amazonian peatland might switch from a current carbon sink into a source in the 21st century.</p><p>Peatlands are important sources and sinks for greenhouse gases (carbon dioxide and methane). Their carbon (C) balance between soil and atmosphere remains unquantified due to the large data gaps and uncertainties in regional peat carbon estimation. My final study was to quantify the C accumulation rates and C stocks within North America peatlands over the last 12,000 years. I find that 85-174 Pg C have been accumulated in North American peatlands over these years including 0.37-0.76 Pg C in subtropical peatlands in this region. During the 10- 8 ka period, the warmer and wetter conditions might have played an important role in stimulating peat C accumulation by enhancing plant photosynthesis. The enhanced peat decomposition due to warming through the Holocene slows down carbon accumulation in the region.</p><div><br></div><p><br></p>