scholarly journals Detected Changes in Precipitation Extremes at Their Native Scales Derived from In Situ Measurements

2019 ◽  
Vol 32 (23) ◽  
pp. 8087-8109 ◽  
Author(s):  
Mark D. Risser ◽  
Christopher J. Paciorek ◽  
Travis A. O’Brien ◽  
Michael F. Wehner ◽  
William D. Collins
Keyword(s):  
Extreme Precipitation ◽  
Seasonal Changes ◽  
Signal To Noise Ratio ◽  
Statistical Technique ◽  
In Situ Measurements ◽  
Absolute Sense ◽  
Underlying Causes ◽  
Changes Over Time ◽  
Future Work

Abstract The gridding of daily accumulated precipitation—especially extremes—from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily datasets are generally underestimated. In this paper, we characterize high-resolution changes in observed extreme precipitation from 1950 to 2017 for the contiguous United States (CONUS) based on in situ measurements only. Our analysis utilizes spatial statistical methods that allow us to derive gridded estimates that do not smooth extreme daily measurements and are consistent with statistics from the original station data while increasing the resulting signal-to-noise ratio. Furthermore, we use a robust statistical technique to identify significant pointwise changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. We present and discuss seasonal changes in the statistics of extreme precipitation: the largest and most spatially coherent pointwise changes are in fall (SON), with approximately 33% of CONUS exhibiting significant changes (in an absolute sense). Other seasons display very few meaningful pointwise changes (in either a relative or absolute sense), illustrating the difficulty in detecting pointwise changes in extreme precipitation based on in situ measurements. While our main result involves seasonal changes, we also present and discuss annual changes in the statistics of extreme precipitation. In this paper we only seek to detect changes over time and leave attribution of the underlying causes of these changes for future work.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

scholarly journals Validation of MOPITT Carbon Monoxide (CO) retrievals over urban regions

2019 ◽  
Author(s):  
Wenfu Tang ◽  
Helen M. Worden ◽  
Merritt N. Deeter ◽  
David P. Edwards ◽  
Louisa K. Emmons ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Urban Regions ◽  
Model Based ◽  
Sensitivity Tests ◽  
Aircraft Observations ◽  
Future Work ◽  
Co Concentrations ◽  
Better Than

Abstract. The performance of the Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions has not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we validate MOPITT products over urban regions using aircraft measurements from DISCOVER-AQ, SEAC4RS, ARIAs, A-FORCE, and KORUS-AQ campaigns. Overall, MOPITT performs reasonably well over both urban and non-urban regions, overall biases for V8J and V8T vary from −0.7 % to 0.0 %, and from 2.0 % to 3.5 %, respectively. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than that over urban regions with smaller biases and higher correlation coefficients. We find that the performance of MOPITT V8J and V8T at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily-polluted scenes. We test the sensitivities of validation results to assumptions and data filters applied during the comparisons of MOPITT retrievals and in-situ profiles. The results at the surface are insensitive to the model-based profile extension (required due to aircraft altitude limitations) whereas the results at levels with limited aircraft observations are more sensitive to the model-based profile extension. The validation results are insensitive to the allowed maximum time difference as criteria for co-location (12 hours, 6 hours, 3 hours, and 1 hour), and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km) and hence the MOPITT retrievals included in the validation become very small. Daytime MOPITT products have overall smaller biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in-situ profiles, likely due to the reduced number of MOPITT retrievals that result for comparison. Comparisons of MOPITT retrievals and in-situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate the some of that errors are due to CO representativeness with these sensitivity tests, but further quantification of validation errors due to CO variability within the MOPITT footprint will require future work.

scholarly journals Arise (Antarctic Remote Ice Sensing Experiment) in the East 2003: validation of Satellite-derived Sea-ice data products

2006 ◽  
Vol 44 ◽  
pp. 288-296 ◽  
Author(s):  
Robert A. Massom ◽  
Anthony Worby ◽  
Victoria Lytle ◽  
Thorsten Markus ◽  
Ian Allison ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
In Situ Measurements ◽  
Aerial Photos ◽  
Ice Concentration ◽  
Data Products ◽  
Surface Measurements ◽  
Future Work ◽  
Snow Thickness

AbstractPreliminary results are presented from the first validation of geophysical data products (ice concentration, Snow thickness on Sea ice (hs) and ice temperature (TI) from the NASA EOS Aqua AMSR-E Sensor, in East Antarctica (in September–October 2003). The challenge of collecting Sufficient measurements with which to validate the coarse-resolution AMSR-E data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other Satellite data. Initial results from a circumnavigation of the experimental Site indicate that, at least under cold conditions with a dry Snow cover, there is a reasonably close agreement between Satellite- and aerial-photo-derived ice concentrations, i.e. 97.2±3.6% for NT2 and 96.5±2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the AMSR-E concentration represents a Slight overestimate of the actual concentration, with the largest discrepancies occurring in regions containing a relatively high proportion of thin ice. The AMSR-E concentrations from the NT2 and BBA algorithms are Similar on average, although differences of up to 5% occur in places, again related to thin-ice distribution. The AMSR-E ice temperature (TI) product agrees with coincident Surface measurements to approximately 0.5˚C in the limited dataset analyzed. Regarding Snow thickness, the AMSR hs retrieval is a Significant underestimate compared to in situ measurements weighted by the percentage of thin ice (and open water) present. For the case Study analyzed, the underestimate was 46% for the overall average, but 23% compared to Smooth-ice measurements. The Spatial distribution of the AMSR-E hs product follows an expected and consistent Spatial pattern, Suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is highlighted.

scholarly journals Average seasonal changes in chlorophyll a in Icelandic waters

2009 ◽  
Vol 66 (10) ◽  
pp. 2133-2140 ◽  
Author(s):  
Kristinn Guðmundsson ◽  
Mike R. Heath ◽  
Elizabeth D. Clarke
Keyword(s):  
Chlorophyll A ◽  
Seasonal Changes ◽  
Chlorophyll Concentration ◽  
Penalized Regression ◽  
In Situ Measurements ◽  
Sampling Location ◽  
Penalized Regression Spline ◽  
Satellite Chlorophyll ◽  
Icelandic Waters

Abstract Guðmundsson, K., Heath, M. R., and Clarke, E. D. 2009. Average seasonal changes in chlorophyll a in Icelandic waters. – ICES Journal of Marine Science, 66: 2133–2140. The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ∼60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.

The Carbon:²³⁴Thorium ratios of sinking particles in the California Current Ecosystem 2: Examination of a thorium sorption, desorption, and particle transport model

2019 ◽  
Author(s):  
Michael Stukel ◽  
Thomas Kelly
Keyword(s):  
Organic Carbon ◽  
Water Column ◽  
Particulate Organic Carbon ◽  
Transport Model ◽  
California Current ◽  
In Situ Measurements ◽  
Power Law Distribution ◽  
Sinking Particles ◽  
Organic Carbon Concentration

Thorium-234 (234Th) is a powerful tracer of particle dynamics and the biological pump in the surface ocean; however, variability in carbon:thorium ratios of sinking particles adds substantial uncertainty to estimates of organic carbon export. We coupled a mechanistic thorium sorption and desorption model to a one-dimensional particle sinking model that uses realistic particle settling velocity spectra. The model generates estimates of 238U-234Th disequilibrium, particulate organic carbon concentration, and the C:234Th ratio of sinking particles, which are then compared to in situ measurements from quasi-Lagrangian studies conducted on six cruises in the California Current Ecosystem. Broad patterns observed in in situ measurements, including decreasing C:234Th ratios with depth and a strong correlation between sinking C:234Th and the ratio of vertically-integrated particulate organic carbon (POC) to vertically-integrated total water column 234Th, were accurately recovered by models assuming either a power law distribution of sinking speeds or a double log normal distribution of sinking speeds. Simulations suggested that the observed decrease in C:234Th with depth may be driven by preferential remineralization of carbon by particle-attached microbes. However, an alternate model structure featuring complete consumption and/or disaggregation of particles by mesozooplankton (e.g. no preferential remineralization of carbon) was also able to simulate decreasing C:234Th with depth (although the decrease was weaker), driven by 234Th adsorption onto slowly sinking particles. Model results also suggest that during bloom decays C:234Th ratios of sinking particles should be higher than expected (based on contemporaneous water column POC), because high settling velocities minimize carbon remineralization during sinking.

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