scholarly journals Sensitivity of the Variability of Mineral Aerosol Simulations to Meteorological Forcing Datasets

2016 ◽  
Author(s):  
Molly B. Smith ◽  
Natalie M. Mahowald ◽  
Samuel Albani ◽  
Aaron Perry ◽  
Remi Losno ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Atmospheric Model ◽  
Reanalysis Dataset ◽  
Meteorological Forcing ◽  
Large Variability ◽  
Desert Dust ◽  
Time Period ◽  
Aerosol Dust

Abstract. Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order to determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well at the limited observational sites available. Overall, aerosol dust source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. The models predict similar seasonal variability and timing as one another, and obtain the best comparison to observations at the seasonal scale. Interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least one year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability appears to be larger, 2–3 years of data are needed.

scholarly journals Sensitivity of the interannual variability of mineral aerosol simulations to meteorological forcing dataset

2017 ◽  
Vol 17 (5) ◽  
pp. 3253-3278 ◽  
Author(s):  
Molly B. Smith ◽  
Natalie M. Mahowald ◽  
Samuel Albani ◽  
Aaron Perry ◽  
Remi Losno ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Atmospheric Model ◽  
Reanalysis Dataset ◽  
Meteorological Forcing ◽  
Large Variability ◽  
Desert Dust ◽  
Time Period ◽  
Aerosol Dust

Abstract. Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order to determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well (or poorly) at the limited observational sites available. Overall, aerosol dust-source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. Model interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least 1 year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability may be larger than in the Northern Hemisphere, 2–3 years of data are likely to be needed.

scholarly journals How much should we believe correlations between Arctic cyclones and sea ice extent?

2017 ◽  
Author(s):  
Jamie G. L. Rae ◽  
Alexander D. Todd ◽  
Edward W. Blockley ◽  
Jeff K. Ridley
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Reanalysis Dataset ◽  
Sea Ice Extent ◽  
Sea Level Pressure ◽  
Temporal Sampling ◽  
Time Period ◽  
Arctic Sea ◽  
Short Time

Abstract. This paper presents an analysis of Arctic summer cyclones in a climate model and in a reanalysis dataset. A cyclone identification and tracking algorithm is run for output from model simulations at two resolutions, and for the reanalysis, using two different tracking variables (mean sea-level pressure and 850 hPa vorticity) for identification of the cyclones. Correlations between characteristics of the cyclones and September Arctic sea ice extent are investigated, and the influence of the tracking variable, the spatial resolution of the model, and spatial and temporal sampling, on the correlations is explored. We conclude that the correlations obtained depend on all of these factors, and that care should be taken when interpreting the results of such analyses, especially when the focus is on one reanalysis, or output from one model, analysed with a single tracking variable for a short time period.

scholarly journals An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 217
Author(s):  
Jiangping Zhu ◽  
Aihong Xie ◽  
Xiang Qin ◽  
Yetang Wang ◽  
Bing Xu ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Antarctic Peninsula ◽  
Austral Summer ◽  
Correlation Coefficients ◽  
East Antarctica ◽  
West Antarctica ◽  
Austral Spring ◽  
Reanalysis Dataset ◽  
Near Surface ◽  
The Antarctic

The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 °C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

scholarly journals A Climatology of 500-hPa Closed Lows in the Northeastern Pacific Ocean, 1948–2011

2014 ◽  
Vol 53 (6) ◽  
pp. 1578-1592 ◽  
Author(s):  
Nina S. Oakley ◽  
Kelly T. Redmond
Keyword(s):  
United States ◽  
Pacific Ocean ◽  
Interannual Variability ◽  
West Coast ◽  
The United States ◽  
Western United States ◽  
The West ◽  
Reanalysis Dataset ◽  
The Pacific ◽  
Northeastern Pacific

AbstractThe northeastern Pacific Ocean is a preferential location for the formation of closed low pressure systems. These slow-moving, quasi-barotropic systems influence vertical stability and sustain a moist environment, giving them the potential to produce or affect sustained precipitation episodes along the west coast of the United States. They can remain motionless or change direction and speed more than once and thus often pose difficult forecast challenges. This study creates an objective climatological description of 500-hPa closed lows to assess their impacts on precipitation in the western United States and to explore interannual variability and preferred tracks. Geopotential height at 500 hPa from the NCEP–NCAR global reanalysis dataset was used at 6-h and 2.5° × 2.5° resolution for the period 1948–2011. Closed lows displayed seasonality and preferential durations. Time series for seasonal and annual event counts were found to exhibit strong interannual variability. Composites of the tracks of landfalling closed lows revealed preferential tracks as the features move inland over the western United States. Correlations of seasonal event totals for closed lows with ENSO indices, the Pacific decadal oscillation (PDO), and the Pacific–North American (PNA) pattern suggested an above-average number of events during the warm phase of ENSO and positive PDO and PNA phases. Precipitation at 30 U.S. Cooperative Observer stations was attributed to closed-low events, suggesting 20%–60% of annual precipitation along the West Coast may be associated with closed lows.

scholarly journals Diurnal variations in middle-atmospheric water vapor by ground-based microwave radiometry

2013 ◽  
Vol 13 (14) ◽  
pp. 6877-6886 ◽  
Author(s):  
D. Scheiben ◽  
A. Schanz ◽  
B. Tschanz ◽  
N. Kämpfer
Keyword(s):  
Water Vapor ◽  
Observational Data ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Diurnal Variations ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Data Set ◽  
Diurnal Variability ◽  
Time Periods

Abstract. In this paper, we compare the diurnal variations in middle-atmospheric water vapor as measured by two ground-based microwave radiometers in the Alpine region near Bern, Switzerland. The observational data set is also compared to data from the chemistry–climate model WACCM. Due to the small diurnal variations of usually less than 1%, averages over extended time periods are required. Therefore, two time periods of five months each, December to April and June to October, were taken for the comparison. The diurnal variations from the observational data agree well with each other in amplitude and phase. The linear correlation coefficients range from 0.8 in the upper stratosphere to 0.5 in the upper mesosphere. The observed diurnal variability is significant at all pressure levels within the sensitivity of the instruments. Comparing our observations with WACCM, we find that the agreement of the phase of the diurnal cycle between observations and model is better from December to April than from June to October. The amplitudes of the diurnal variations for both time periods increase with altitude in WACCM, but remain approximately constant at 0.05 ppm in the observations. The WACCM data are used to separate the processes that lead to diurnal variations in middle-atmospheric water vapor above Bern. The dominating processes were found to be meridional advection below 0.1 hPa, vertical advection between 0.1 and 0.02 hPa and (photo-)chemistry above 0.02 hPa. The contribution of zonal advection is small. The highest diurnal variations in water vapor as seen in the WACCM data are found in the mesopause region during the time period from June to October with diurnal amplitudes of 0.2 ppm (approximately 5% in relative units).

Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms

2012 ◽  
Vol 43 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Manish Kumar Goyal ◽  
C. S. P. Ojha
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Lake Basin ◽  
Monthly Precipitation ◽  
M5 Model Tree ◽  
Reanalysis Dataset ◽  
Model Tree ◽  
Basin Scale ◽  
M5 Model

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.

scholarly journals Dynamic Downscaling of the Impact of Climate Change on the Ocean Circulation in the Galápagos Archipelago

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyun Liu ◽  
Lian Xie ◽  
John M. Morrison ◽  
Daniel Kamykowski
Keyword(s):  
Climate Change ◽  
Ocean Circulation ◽  
Global Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Ocean Model ◽  
Reanalysis Dataset ◽  
Equatorial Undercurrent ◽  
Galapagos Archipelago ◽  
The Impact

The regional impact of global climate change on the ocean circulation around the Galápagos Archipelago is studied using the Hybrid Coordinate Ocean Model (HYCOM) configured for a four-level nested domain system. The modeling system is validated and calibrated using daily atmospheric forcing derived from the NCEP/NCAR reanalysis dataset from 1951 to 2007. The potential impact of future anthropogenic global warming (AGW) in the Galápagos region is examined using the calibrated HYCOM with forcing derived from the IPCC-AR4 climate model. Results show that although the oceanic variability in the entire Galápagos region is significantly affected by global climate change, the degree of such effects is inhomogeneous across the region. The upwelling region to the west of the Isabella Island shows relatively slower warming trends compared to the eastern Galápagos region. Diagnostic analysis suggests that the variability in the western Galápagos upwelling region is affected mainly by equatorial undercurrent (EUC) and Panama currents, while the central/east Galápagos is predominantly affected by both Peru and EUC currents. The inhomogeneous responses in different regions of the Galápagos Archipelago to future AGW can be explained by the incoherent changes of the various current systems in the Galápagos region as a result of global climate change.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

&lt;p&gt;Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.&lt;/p&gt;&lt;p&gt;Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.&lt;/p&gt;&lt;p&gt;We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document