Terrestrial Vegetation Dynamics and Global Climate Controls in North America: 2001–05

2008 ◽  
Vol 12 (8) ◽  
pp. 1-12 ◽  
Author(s):  
Christopher Potter ◽  
Shyam Boriah ◽  
Michael Steinbach ◽  
Vipin Kumar ◽  
Steven Klooster
Keyword(s):  
North America ◽  
Vegetation Dynamics ◽  
Southern Oscillation ◽  
Global Climate ◽  
Regional Climate ◽  
The United States ◽  
Terrestrial Vegetation ◽  
Northern Rocky Mountains ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Monthly composite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor was used to reconstruct vegetation dynamics in response to climate patterns over the period 2001–05 for North America. Results imply that plant growth over extensive land areas were closely coupled to El Niño–Southern Oscillation (ENSO) effects on regional climate. Areas strongly tied to recent (2002–03) ENSO climate effects were located mainly in northwestern Canada, interior Alaska, the northern Rocky Mountains of the United States, and throughout northern Mexico. Localized variations in precipitation were detected as the predominant controllers of monthly values for the MODIS fraction absorbed of photosynthetically active radiation (FPAR) over these regions.

Terrestrial Vegetation Dynamics and Global Climate Controls in North America: 2001–2005

2008 ◽  
Vol preprint (2008) ◽  
pp. 1
Author(s):  
Christopher Potter ◽  
Shyam Boriah ◽  
Michael Steinbach ◽  
Vipin Kumar ◽  
Steven Klooster
Keyword(s):  
North America ◽  
Vegetation Dynamics ◽  
Global Climate ◽  
Terrestrial Vegetation ◽  
Climate Controls

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

Drivers of Twenty-First Century U.S. Winter Precipitation Trends in CMIP6 Models: A Storyline-Based Approach

2021 ◽  
pp. 1-48
Author(s):  
Daniel F. Schmidt ◽  
Kevin M. Grise
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
The United States ◽  
Winter Precipitation ◽  
Global Climate Models ◽  
First Century ◽  
Precipitation Trend ◽  
Twenty First Century ◽  
Precipitation Trends

AbstractClimate change during the twenty-first century has the potential to substantially alter geographic patterns of precipitation. However, regional precipitation changes can be very difficult to project, and in some regions, global climate models do not even agree on the sign of the precipitation trend. Since some of this uncertainty is due to internal variability rather than model bias, models cannot be used to narrow the possibilities to a single outcome, but they can usefully quantify the range of plausible outcomes and identify the combination of dynamical drivers that would be likely to produce each.This study uses a storylines approach—a type of regression-based analysis—to identify some of the key dynamical drivers that explain the variance in 21st century U.S. winter precipitation trends across CMIP6 models under the SSP3-7.0 emissions scenario. This analysis shows that the spread in precipitation trends is not primarily driven by differences in modeled climate sensitivity. Key drivers include global-mean surface temperature, but also tropical upper-troposphere temperature, the El Niño-Southern Oscillation (ENSO), the Pacific-North America (PNA) pattern, and the East Pacific (EP) dipole (a dipole pattern in geopotential heights over North America’s Pacific coast). Combinations of these drivers can reinforce or cancel to produce various high- or low-impact scenarios for winter precipitation trends in various regions of the United States. For example, the most extreme winter precipitation trends in the southwestern U.S. result from opposite trends in ENSO and EP, whereas the wettest winter precipitation trends in the midwestern U.S. result from a combination of strong global warming and a negative PNA trend.

scholarly journals The potential effects of climate change on air quality across the conterminous U.S. at 2030 under three Representative Concentration Pathways (RCPs)

2018 ◽  
Author(s):  
Christopher G. Nolte ◽  
Tanya L. Spero ◽  
Jared H. Bowden ◽  
Megan S. Mallard ◽  
Patrick D. Dolwick
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Global Climate ◽  
Regional Climate ◽  
The United States ◽  
Historical Period ◽  
Daily Maximum ◽  
Good Representation ◽  
Representative Concentration Pathways ◽  
Concentration Changes

Abstract. The potential impacts of climate change on regional ozone (O3) and fine particulate (PM2.5) air quality in the United States are investigated by downscaling Community Earth System Model (CESM) global climate simulations with the Weather Research and Forecasting (WRF) model, then using the downscaled meteorological fields with the Community Multiscale Air Quality (CMAQ) model. Regional climate and air quality change between 2000 and 2030 under three Representative Concentration Pathways (RCPs) is simulated using 11-year time slices from CESM. The regional climate fields represent historical daily maximum and daily minimum temperatures well, with mean biases less than 2 K for most regions of the U.S. and most seasons of the year and good representation of the variability. Precipitation in the central and eastern U.S. is well simulated for the historical period, with seasonal and annual biases generally less than 25 %, and positive biases in the western U.S. throughout the year and in part of the eastern U.S. during summer. Maximum daily 8-h ozone (MDA8 O3) is projected to increase during summer and autumn in the central and eastern U.S. The increase in summer mean MDA8 O3 is largest under RCP8.5, exceeding 4 ppb in some locations, with smaller seasonal mean increases of up to 2 ppb simulated during autumn and changes during spring generally less than 1 ppb. Increases are magnified at the upper end of the O3 distribution, particularly where projected increases in temperature are greater. Annual average PM2.5 concentration changes range from −1.0 to 1.0 μg m−3. Organic PM2.5 concentrations increase during summer and autumn due to increased biogenic emissions. Decreases in aerosol nitrate occur during winter, accompanied by lesser decreases in ammonium and sulfate, due to warmer temperatures causing increased partitioning to the gas phase. Among meteorological factors examined to account for modeled changes in pollution, temperature and isoprene emissions are found to have the largest changes and the greatest impact on O3 concentrations.

scholarly journals A Decade of the Moderate Resolution Imaging Spectroradiometer: Is a Solar–Cloud Link Detectable?

2012 ◽  
Vol 25 (13) ◽  
pp. 4430-4440 ◽  
Author(s):  
Benjamin Laken ◽  
Enric Pallé ◽  
Hiroko Miyahara
Keyword(s):  
Southern Oscillation ◽  
Cosmic Ray ◽  
Total Solar Irradiance ◽  
Recent Climate ◽  
Primary Driver ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global And Local ◽  
Galactic Cosmic Ray

Abstract Based on the results of decadal correlation studies between the International Satellite Cloud Climatology Project–detected cloud anomalies and the galactic cosmic ray (GCR) flux, it has been suggested that a relationship exists between solar activity and cloud cover. If valid, such a relationship could have important implications for scientists’ understanding of recent climate change. In this work, an analysis of the first decade of Moderate Resolution Imaging Spectroradiometer (MODIS)–detected cloud anomalies are presented, and the data at global and local geographical resolutions to total solar irradiance (TSI), GCR variations, and the multivariate El Niño–Southern Oscillation index are compared. The study identifies no statistically significant correlations between cloud anomalies and TSI/GCR variations, and concludes that solar-related variability is not a primary driver of monthly to annual MODIS cloud variability. The authors observe a net increase in cloud detected by MODIS over the past decade of ~0.58%, arising from a combination of a reduction in high- to midlevel cloud (−0.31%) and an increase in low-level cloud (0.89%); these long-term changes may be largely attributed to ENSO-induced cloud variability.

scholarly journals On the Seasonal Forecasting of Regional Tropical Cyclone Activity

2014 ◽  
Vol 27 (21) ◽  
pp. 7994-8016 ◽  
Author(s):  
G. A. Vecchi ◽  
T. Delworth ◽  
R. Gudgel ◽  
S. Kapnick ◽  
A. Rosati ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Global Climate ◽  
Spatial Scales ◽  
Global Climate Model ◽  
Regional Climate ◽  
Weather Extremes ◽  
Moderate Resolution ◽  
Systematic Biases ◽  
Constrained Conditions

Abstract Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

scholarly journals US particulate matter air quality improves except in wildfire-prone areas

2018 ◽  
Vol 115 (31) ◽  
pp. 7901-7906 ◽  
Author(s):  
Crystal D. McClure ◽  
Daniel A. Jaffe
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
The United States ◽  
Total Carbon ◽  
Positive Trend ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Using Data

Using data from rural monitoring sites across the contiguous United States, we evaluated fine particulate matter (PM2.5) trends for 1988–2016. We calculate trends in the policy-relevant 98th quantile of PM2.5 using Quantile Regression. We use Kriging and Gaussian Geostatistical Simulations to interpolate trends between observed data points. Overall, we found positive trends in 98th quantile PM2.5 at sites within the Northwest United States (average 0.21 ± 0.12 µg·m−3·y−1; ±95% confidence interval). This was in contrast with sites throughout the rest of country, which showed a negative trend in 98th quantile PM2.5, likely due to reductions in anthropogenic emissions (average −0.66 ± 0.10 µg·m−3·y−1). The positive trend in 98th quantile PM2.5 is due to wildfire activity and was supported by positive trends in total carbon and no trend in sulfate across the Northwest. We also evaluated daily moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) for 2002–2017 throughout the United States to compare with ground-based trends. For both Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5 and MODIS AOD datasets, we found positive 98th quantile trends in the Northwest (1.77 ± 0.68% and 2.12 ± 0.81% per year, respectively) through 2016. The trend in Northwest AOD is even greater if data for the high-fire year of 2017 are included. These results indicate a decrease in PM2.5 over most of the country but a positive trend in the 98th quantile PM2.5 across the Northwest due to wildfires.

scholarly journals Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 362
Author(s):  
Jihui Yuan
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Global Climate ◽  
Temporal Changes ◽  
Spatial And Temporal Changes ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite

Currently, global climate change (GCC) and the urban heat island (UHI) phenomena are becoming serious problems, partly due to the artificial construction of the land surface. When sunlight reaches the land surface, some of it is absorbed and some is reflected. The state of the land surface directly affects the surface albedo, which determines the magnitude of solar radiation reflected by the land surface in the daytime. In order to better understand the spatial and temporal changes in surface albedo, this study investigated and analyzed the surface albedo from 2000 to 2016 (2000, 2008, and 2016) in the entire Chinese territory, based on the measurement database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASA’s Terra satellite. It was shown that the Northeast China exhibited the largest decline in surface albedo and North China showed the largest rising trend of surface albedo from 2000 to 2016. The correlation between changes in surface albedo and the Normalized Difference Vegetation Index (NDVI) indicated that the change trend of surface albedo was opposite to that of NDVI. In addition, in order to better understand the distribution of surface albedo in the entire Chinese territory, the classifications of surface albedo in three years (2000, 2008, and 2016) were implemented using five classification methods in this study.

scholarly journals Future Decreases in Freezing Days across North America

2016 ◽  
Vol 29 (19) ◽  
pp. 6923-6935 ◽  
Author(s):  
Michael A. Rawlins ◽  
Raymond S. Bradley ◽  
Henry F. Diaz ◽  
John S. Kimball ◽  
David A. Robinson
Keyword(s):  
North America ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Rate Of Change ◽  
Mean Annual Temperature ◽  
The North ◽  
Air Temperatures ◽  
Climate Change Assessment

Abstract This study used air temperatures from a suite of regional climate models participating in the North American Climate Change Assessment Program (NARCCAP) together with two atmospheric reanalysis datasets to investigate changes in freezing days (defined as days with daily average temperature below freezing) likely to occur between 30-yr baseline (1971–2000) and midcentury (2041–70) periods across most of North America. Changes in NARCCAP ensemble mean winter temperature show a strong gradient with latitude, with warming of over 4°C near Hudson Bay. The decline in freezing days ranges from less than 10 days across north-central Canada to nearly 90 days in the warmest areas of the continent that currently undergo seasonally freezing conditions. The area experiencing freezing days contracts by 0.9–1.0 × 106 km2 (5.7%–6.4% of the total area). Areas with mean annual temperature between 2° and 6°C and a relatively low rate of change in climatological daily temperatures (<0.2°C day−) near the time of spring thaw will encounter the greatest decreases in freezing days. Advances in the timing of spring thaw will exceed the delay in fall freeze across much of the United States, with the reverse pattern likely over most of Canada.

Sign in / Sign up

Export Citation Format

Share Document