Tropospheric Age‐of‐Air: Influence of Emissions on Recent Surface Trends and Model Biases

2021 ◽  
Author(s):  
Clara Orbe ◽  
Darryn W. Waugh ◽  
Stephen Montzka ◽  
Edward J. Dlugokencky ◽  
Susan Strahan ◽  
...  
Keyword(s):  
Model Biases

scholarly journals Measuring Model Biases in the Absence of Ground Truth

2021 ◽  
Author(s):  
Osman Aka ◽  
Ken Burke ◽  
Alex Bauerle ◽  
Christina Greer ◽  
Margaret Mitchell
Keyword(s):  
Ground Truth ◽  
Model Biases

Climate change in the High Mountain Asia simulated with CMIP6 models

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate Models ◽  
Cold Bias ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Model Biases ◽  
Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 °C (± 0.5 °C), 3.4 °C (± 0.7 °C), 5.2 °C (± 1.2 °C), and 6.6 °C (± 1.5 °C); a relative decrease in the snow cover extent of 10 % (± 4.1 %), 19 % (± 5 %), 29 % (± 8 %), and 35 % (± 9 %); and an increase in total precipitation of 9 % (± 5 %), 13 % (± 7 %), 19 % (± 11 %), and 27 % (± 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

scholarly journals Average versus high surface ozone levels over the continental U.S.A.: Model bias, background influences, and interannual variability

2018 ◽  
Author(s):  
Jean J. Guo ◽  
Arlene M. Fiore ◽  
Lee T. Murray ◽  
Daniel A. Jaffe ◽  
Jordan L. Schnell ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Transport Model ◽  
Surface Ozone ◽  
High Surface ◽  
Anthropogenic Sources ◽  
Pollution Transport ◽  
Natural Sources ◽  
Model Biases ◽  
Regional Sources ◽  
Surface O3

Abstract. U.S. background ozone (O3) includes O3 produced from anthropogenic O3 precursors emitted outside of the U.S.A., from global methane, and from any natural sources. Using a suite of sensitivity simulations in the GEOS-Chem global chemistry-transport model, we estimate the influence from individual background versus U.S. anthropogenic sources on total surface O3 over ten continental U.S. regions from 2004–2012. Evaluation with observations reveals model biases of +0–19 ppb in seasonal mean maximum daily 8-hour average (MDA8) O3, highest in summer over the eastern U.S.A. Simulated high-O3 events cluster too late in the season. We link these model biases to regional O3 production (e.g., U.S. anthropogenic, biogenic volatile organic compounds (BVOC), and soil NOx, emissions), or coincident missing sinks. On the ten highest observed O3 days during summer (O3_top10obs_JJA), U.S. anthropogenic emissions enhance O3 by 5–11 ppb and by less than 2 ppb in the eastern versus western U.S.A. The O3 enhancement from BVOC emissions during summer is 1–7 ppb higher on O3_top10obs_JJA days than on average days, while intercontinental pollution is up to 2 ppb higher on average vs. on O3_top10obs_JJA days. In the model, regional sources of O3 precursor emissions drive interannual variability in the highest observed O3 levels. During the summers of 2004–2012, monthly regional mean U.S. background O3 MDA8 levels vary by 10–20 ppb. Simulated summertime total surface O3 levels on O3_top10obs_JJA days decline by 3 ppb (averaged over all regions) from 2004–2006 to 2010–2012 in both the observations and the model, reflecting rising U.S. background (+2 ppb) and declining U.S. anthropogenic O3 emissions (−6 ppb). The model attributes interannual variability in U.S. background O3 on O3_top10obs days to natural sources, not international pollution transport. We find that a three-year averaging period is not long enough to eliminate interannual variability in background O3.

scholarly journals On the Sensitivity of the Simulated Diurnal Cycle of Precipitation to 3-Hourly Radiosonde Assimilation: A Case Study over the Western Maritime Continent

2021 ◽  
Vol 149 (10) ◽  
pp. 3449-3468
Author(s):  
Joshua Chun Kwang Lee ◽  
Anurag Dipankar ◽  
Xiang-Yu Huang
Keyword(s):  
Diurnal Cycle ◽  
High Frequency ◽  
Initial Conditions ◽  
Rainfall Variability ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Western Coast ◽  
Maritime Continent ◽  
Model Biases ◽  
Diurnal Cycle Of Precipitation

AbstractThe diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.

Omens of coupled model biases in the CMIP5 AMIP simulations

2018 ◽  
Vol 51 (7-8) ◽  
pp. 2927-2941 ◽  
Author(s):  
Alina Găinuşă-Bogdan ◽  
Frédéric Hourdin ◽  
Abdoul Khadre Traore ◽  
Pascale Braconnot
Keyword(s):  
Coupled Model ◽  
Model Biases

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

2018 ◽  
Vol 31 (16) ◽  
pp. 6591-6610 ◽  
Author(s):  
Martin Aleksandrov Ivanov ◽  
Jürg Luterbacher ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Analytical Description ◽  
Analytical Theory ◽  
Climate Change Signal ◽  
Future Research ◽  
Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

scholarly journals Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

2020 ◽  
Vol 47 (5) ◽  
Author(s):  
Hui Ding ◽  
Matthew Newman ◽  
Michael A. Alexander ◽  
Andrew T. Wittenberg
Keyword(s):  
Seasonal Forecast ◽  
Tropical Pacific ◽  
Forecast Skill ◽  
Cmip5 Model ◽  
Model Biases ◽  
The Tropical Pacific

scholarly journals Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

2012 ◽  
Vol 51 (10) ◽  
pp. 1835-1854 ◽  
Author(s):  
Jure Cedilnik ◽  
Dominique Carrer ◽  
Jean-François Mahfouf ◽  
Jean-Louis Roujean
Keyword(s):  
Land Surface ◽  
Short Range ◽  
Surface Albedo ◽  
Weather Prediction ◽  
A Priori ◽  
Limited Area ◽  
Model Biases ◽  
Nwp Model ◽  
The Impact ◽  
Range Forecasts

AbstractThis study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.

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