scholarly journals Implications of model bias in carbon monoxide for methane lifetime

2015 ◽  
Vol 15 (14) ◽  
pp. 20305-20348 ◽  
Author(s):  
S. A. Strode ◽  
B. N. Duncan ◽  
E. A. Yegorova ◽  
J. Kouatchou ◽  
J. R. Ziemke ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Hemispheric Asymmetry ◽  
Climate Models ◽  
Model Bias ◽  
Source Type ◽  
Methyl Chloroform ◽  
Northern Latitudes ◽  
High Bias ◽  
The Impact ◽  
Global Mean

Abstract. A low bias in carbon monoxide (CO) at high northern latitudes is a common feature of chemistry climate models (CCMs) that may indicate or contribute to a high bias in simulated OH and corresponding low bias in methane lifetime. We use simulations with CO tagged by source type to investigate the sensitivity of the CO bias to CO emissions, global mean OH, and the hemispheric asymmetry of OH. Our results show that reducing the hemispheric asymmetry of OH improves the agreement of simulated CO with observations. We use simulations with parameterized OH to quantify the impact of known model biases on simulated OH. Removing biases in ozone and water vapor as well as reducing Northern Hemisphere NOx does not remove the hemispheric asymmetry in OH, but brings the simulated methyl chloroform lifetime into agreement with observation-based estimates.

scholarly journals Implications of carbon monoxide bias for methane lifetime and atmospheric composition in chemistry climate models

2015 ◽  
Vol 15 (20) ◽  
pp. 11789-11805 ◽  
Author(s):  
S. A. Strode ◽  
B. N. Duncan ◽  
E. A. Yegorova ◽  
J. Kouatchou ◽  
J. R. Ziemke ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Hemispheric Asymmetry ◽  
Climate Models ◽  
Atmospheric Composition ◽  
Source Type ◽  
Free Running ◽  
High Bias ◽  
The Impact ◽  
Global Mean ◽  
Surface Observations

Abstract. A low bias in carbon monoxide (CO) at northern high and mid-latitudes is a common feature of chemistry climate models (CCMs) that may indicate or contribute to a high bias in simulated OH and corresponding low bias in methane lifetime. We use simulations with CO tagged by source type to investigate the sensitivity of the CO bias to CO emissions, transport, global mean OH, and the hemispheric asymmetry of OH. We also investigate how each of these possible contributors to the CO bias affects the methane lifetime. We find that the use of specified meteorology alters the distribution of CO compared to a free-running CCM simulation, improving the comparison with surface observations in summer. Our results also show that reducing the hemispheric asymmetry of OH improves the agreement of simulated CO with observations. We use simulations with parameterized OH to quantify the impact of known model biases on simulated OH. Removing biases in ozone and water vapor as well as reducing Northern Hemisphere NOx does not remove the hemispheric asymmetry in OH, but it reduces global mean OH by 18 %, bringing the simulated methane lifetime into agreement with observation-based estimates.

scholarly journals The impact of orbital sampling, monthly averaging and vertical resolution on climate chemistry model evaluation with satellite observations

2011 ◽  
Vol 11 (3) ◽  
pp. 9705-9742
Author(s):  
A. M. Aghedo ◽  
K. W. Bowman ◽  
D. T. Shindell ◽  
G. Faluvegi
Keyword(s):  
Climate Change ◽  
Boundary Layer ◽  
Carbon Monoxide ◽  
Water Vapour ◽  
Climate Models ◽  
Atmospheric Temperature ◽  
Satellite Observations ◽  
Vertical Resolution ◽  
Upper Troposphere ◽  
The Impact

Abstract. Ensemble climate model simulations used for the Intergovernmental Panel on Climate Change (IPCC) assessments have become important tools for exploring the response of the Earth System to changes in anthropogenic and natural forcings. The systematic evaluation of these models through global satellite observations is a critical step in assessing the uncertainty of climate change projections. This paper presents the technical steps required for using nadir sun-synchronous infrared satellite observations for multi-model evaluation and the uncertainties associated with each step. This is motivated by need to use satellite observations to evaluate climate models. We quantified the implications of the effect of satellite orbit and spatial coverage, the effect of variations in vertical sensitivity as quantified by the observation operator and the impact of averaging the operators for use with monthly-mean model output. We calculated these biases in ozone, carbon monoxide, atmospheric temperature and water vapour by using the output from two global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and the observations from the Tropospheric Emission Spectrometer (TES) satellite from January 2005 to December 2008. The results show that sampling and monthly averaging of the observation operators produce biases of less than ±3% for ozone and carbon monoxide throughout the entire troposphere in both models. Water vapour sampling biases were also within the insignificant range of ±3% (that is ±0.14 g kg−1) in both models. Sampling led to a temperature bias of ±0.3 K over the tropical and mid-latitudes in both models, and up to −1.4 K over the boundary layer in the higher latitudes. Using the monthly average of temperature and water vapour operators lead to large biases over the boundary layer in the southern-hemispheric higher latitudes and in the upper troposphere, respectively. Up to 8% bias was calculated in the upper troposphere water vapour due to monthly-mean operators, which may impact the detection of water vapour feedback in response to global warming. Our results reveal the importance of using the averaging kernel and the a priori profiles to account for the limited vertical resolution of a nadir observation during model application. Neglecting the observation operators resulted in large biases, which are more than 60% for ozone, ±30% for carbon monoxide, and range between −1.5 K and 5 K for atmospheric temperature, and between −60% and 100% for water vapour.

scholarly journals The impact of orbital sampling, monthly averaging and vertical resolution on climate chemistry model evaluation with satellite observations

2011 ◽  
Vol 11 (13) ◽  
pp. 6493-6514 ◽  
Author(s):  
A. M. Aghedo ◽  
K. W. Bowman ◽  
D. T. Shindell ◽  
G. Faluvegi
Keyword(s):  
Climate Change ◽  
Boundary Layer ◽  
Carbon Monoxide ◽  
Water Vapour ◽  
Climate Models ◽  
Atmospheric Temperature ◽  
Satellite Observations ◽  
Vertical Resolution ◽  
Upper Troposphere ◽  
The Impact

Abstract. Ensemble climate model simulations used for the Intergovernmental Panel on Climate Change (IPCC) assessments have become important tools for exploring the response of the Earth System to changes in anthropogenic and natural forcings. The systematic evaluation of these models through global satellite observations is a critical step in assessing the uncertainty of climate change projections. This paper presents the technical steps required for using nadir sun-synchronous infrared satellite observations for multi-model evaluation and the uncertainties associated with each step. This is motivated by need to use satellite observations to evaluate climate models. We quantified the implications of the effect of satellite orbit and spatial coverage, the effect of variations in vertical sensitivity as quantified by the observation operator and the impact of averaging the operators for use with monthly-mean model output. We calculated these biases in ozone, carbon monoxide, atmospheric temperature and water vapour by using the output from two global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and the observations from the Tropospheric Emission Spectrometer (TES) instrument on board the NASA-Aura satellite from January 2005 to December 2008. The results show that sampling and monthly averaging of the observation operators produce zonal-mean biases of less than ±3 % for ozone and carbon monoxide throughout the entire troposphere in both models. Water vapour sampling zonal-mean biases were also within the insignificant range of ±3 % (that is ±0.14 g kg−1) in both models. Sampling led to a temperature zonal-mean bias of ±0.3 K over the tropical and mid-latitudes in both models, and up to −1.4 K over the boundary layer in the higher latitudes. Using the monthly average of temperature and water vapour operators lead to large biases over the boundary layer in the southern-hemispheric higher latitudes and in the upper troposphere, respectively. Up to 8 % bias was calculated in the upper troposphere water vapour due to monthly-mean operators, which may impact the detection of water vapour feedback in response to global warming. Our results reveal the importance of using the averaging kernel and the a priori profiles to account for the limited vertical resolution and clouds of a nadir observation during model application. Neglecting the observation operators resulted in large biases, which are more than 60 % for ozone, ±30 % for carbon monoxide, and range between −1.5 K and 5 K for atmospheric temperature, and between −60 % and 100 % for water vapour.

scholarly journals Last interglacial model–data mismatch of thermal maximum temperatures partially explained

2014 ◽  
Vol 10 (4) ◽  
pp. 1633-1644 ◽  
Author(s):  
P. Bakker ◽  
H. Renssen
Keyword(s):  
Climate Models ◽  
Temporal Variations ◽  
Model Data ◽  
Last Interglacial ◽  
Underlying Assumption ◽  
Thermal Maximum ◽  
Spatio Temporal ◽  
Maximum Temperatures ◽  
The Impact ◽  
Global Mean

Abstract. The timing of the last interglacial (LIG) thermal maximum across the globe remains to be precisely assessed. Because of difficulties in establishing a common temporal framework between records from different palaeoclimatic archives retrieved from various places around the globe, it has not yet been possible to reconstruct spatio-temporal variations in the occurrence of the maximum warmth across the globe. Instead, snapshot reconstructions of warmest LIG conditions have been presented, which have an underlying assumption that maximum warmth occurred synchronously everywhere. Although known to be an oversimplification, the impact of this assumption on temperature estimates has yet to be assessed. We use the LIG temperature evolutions simulated by nine different climate models to investigate whether the assumption of synchronicity results in a sizeable overestimation of the LIG thermal maximum. We find that for annual temperatures, the overestimation is small, strongly model-dependent (global mean 0.4 ± 0.3 °C) and cannot explain the recently published 0.67 °C difference between simulated and reconstructed annual mean temperatures during the LIG thermal maximum. However, if one takes into consideration that temperature proxies are possibly biased towards summer, the overestimation of the LIG thermal maximum based on warmest month temperatures is non-negligible with a global mean of 1.1 ± 0.4 °C.

scholarly journals A Stochastic Analysis of the Impact of Small-Scale Fluctuations on the Tropospheric Temperature Response to CO2 Doubling

2010 ◽  
Vol 23 (9) ◽  
pp. 2307-2319 ◽  
Author(s):  
Rita Seiffert ◽  
Jin-Song von Storch
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Small Scale ◽  
Tropospheric Temperature ◽  
Climate Response ◽  
Restoring Force ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
The Impact ◽  
Global Mean

Abstract The climate response to increased CO2 concentration is generally studied using climate models that have finite spatial and temporal resolutions. Different parameterizations of the effect of unresolved processes can result in different representations of small-scale fluctuations in the climate model. The representation of small-scale fluctuations can, on the other hand, affect the modeled climate response. In this study the mechanisms by which enhanced small-scale fluctuations alter the climate response to CO2 doubling are investigated. Climate experiments with preindustrial and doubled CO2 concentrations obtained from a comprehensive climate model [ECHAM5/Max Planck Institute Ocean Model (MPI-OM)] are analyzed both with and without enhanced small-scale fluctuations. By applying a stochastic model to the experimental results, two different mechanisms are found. First, the small-scale fluctuations can change the statistical behavior of the global mean temperature as measured by its statistical damping. The statistical damping acts as a restoring force that determines, according to the fluctuation–dissipation theory, the amplitude of the climate response to a change in external forcing (here, CO2 doubling). Second, the small-scale fluctuations can affect processes that occur only in response to the CO2 increase, thereby altering the change of the effective forcing on the global mean temperature.

scholarly journals Projecting Global Mean Sea-Level Change Using CMIP6 Models

2021 ◽  
Author(s):  
Tim Hermans ◽  
Jonathan Gregory ◽  
Matthew Palmer ◽  
Mark Ringer ◽  
Caroline Katsman ◽  
...  
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Sea Level Change ◽  
Global Climate Models ◽  
Mean Sea Level ◽  
Level Change ◽  
Global Mean Sea Level ◽  
The Impact ◽  
Global Mean ◽  
Emissions Scenario

<p>The effective climate sensitivity (EffCS) of models in the Coupled Model Intercomparison Project 6 (CMIP6) has increased relative to CMIP5. Consequently, using CMIP6 models tends to lead to larger projections of global mean surface air temperature (GSAT) increase for a given emissions scenario. The effect of increased EffCS on projections of global mean sea-level (GMSL) change however, has so far only been studied using a reduced complexity model. Here, we explore the implications of increased EffCS in CMIP6 models for GMSL change projections in 2100 for three emissions scenarios: SSP5-8.5, SSP2-4.5 and SSP1-2.6.</p><p>Whereas CMIP6 projections of GSAT change are substantially higher than in CMIP5, projections of global mean thermal expansion (GTE) are only slightly higher. We use these projections as input to construct projections of GMSL change, using the Monte Carlo approach of IPCC AR5. Isolating the impact of the CMIP6 simulations using consistent methods is an important step to ensure traceability to past IPCC projections of global and regional sea-level change. The resulting 95<sup>th</sup> percentile of projected GMSL change at 2100 is only 3-7 cm higher for CMIP6 than for CMIP5, depending on the emissions scenario. Projected rates of GMSL rise around 2100 increase more strongly from CMIP5 to CMIP6, though, implying more pronounced differences beyond 2100 and greater committed sea-level rise. GMSL change in 2100 is accurately predicted by time-integrated temperature change and therefore mitigation requires early reduction of emissions.</p><p>We also find that the 95<sup>th</sup> percentile projections based on individual CMIP6 models can differ as much as 51 cm and that the 5-95% range of projected GMSL change for individual CMIP6 models can be substantially outside of the 5-95% range of the CMIP6 multi-model ensemble. Thus, through subsetting the CMIP6 ensemble using EffCS, a choice can be made between characterizing the central part of the probability distribution and more comprehensively sampling the high end of the GMSL projection space, which is relevant to risk-averse stakeholders. Our results show this may substantially alter ensemble projections, underlining the need to constrain EffCS in global climate models in order to reduce uncertainty in sea-level projections.</p>

scholarly journals Last Interglacial model-data mismatch of thermal maximum temperatures partially explained

2014 ◽  
Vol 10 (1) ◽  
pp. 739-760 ◽  
Author(s):  
P. Bakker ◽  
H. Renssen
Keyword(s):  
Climate Models ◽  
Global Model ◽  
Model Data ◽  
Last Interglacial ◽  
Thermal Maximum ◽  
Maximum Temperatures ◽  
The Impact ◽  
Global Mean

Abstract. The timing of the Last Interglacial (LIG) thermal maximum is highly uncertain. Compilations of maximum LIG temperatures are therefore based on the assumption that maximum warmth occurred synchronously across the globe. Although known to be an oversimplification, the impact of this assumption on temperature estimates has yet to be assessed. We use the LIG temperature evolutions simulated by 9 different climate models to investigate whether the assumption of synchronicity results in a sizeable overestimation of LIG thermal maximum temperatures. We find that for annual temperatures, the overestimation is small, strongly model-dependent (global mean 0.4 ± 0.3 °C) and cannot explain the recently published 0.67 °C difference between simulated and reconstructed LIG thermal maximum temperatures. However, if one takes into consideration that temperature proxies are possibly biased towards summer, the overestimation of the LIG thermal maximum based on warmest month temperatures is non-negligible (global mean 1.1 ± 0.4 °C) and can at least partly explain the 0.67 °C global model-data difference.

scholarly journals Reconciling global mean and regional sea level change in projections and observations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jinping Wang ◽  
John A. Church ◽  
Xuebin Zhang ◽  
Xianyao Chen
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Tide Gauge ◽  
Sea Level Change ◽  
Global Network ◽  
Weighted Mean ◽  
Level Change ◽  
Regional Sea Level ◽  
Global Mean ◽  
Regional Sea Level Change

AbstractThe ability of climate models to simulate 20th century global mean sea level (GMSL) and regional sea-level change has been demonstrated. However, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) sea-level projections have not been rigorously evaluated with observed GMSL and coastal sea level from a global network of tide gauges as the short overlapping period (2007–2018) and natural variability make the detection of trends and accelerations challenging. Here, we critically evaluate these projections with satellite and tide-gauge observations. The observed trends from GMSL and the regional weighted mean at tide-gauge stations confirm the projections under three Representative Concentration Pathway (RCP) scenarios within 90% confidence level during 2007–2018. The central values of the observed GMSL (1993–2018) and regional weighted mean (1970–2018) accelerations are larger than projections for RCP2.6 and lie between (or even above) those for RCP4.5 and RCP8.5 over 2007–2032, but are not yet statistically different from any scenario. While the confirmation of the projection trends gives us confidence in current understanding of near future sea-level change, it leaves open questions concerning late 21st century non-linear accelerations from ice-sheet contributions.

Comparison of a preferred versus non-preferred waterpipe tobacco flavour: subjective experience, smoking behaviour and toxicant exposure

2017 ◽  
Vol 27 (3) ◽  
pp. 319-324 ◽  
Author(s):  
Eleanor L Leavens ◽  
Leslie M Driskill ◽  
Neil Molina ◽  
Thomas Eissenberg ◽  
Alan Shihadeh ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Subjective Experience ◽  
Smoking Behaviour ◽  
Toxicant Exposure ◽  
P Values ◽  
Smoking Topography ◽  
Exhaled Carbon Monoxide ◽  
Continued Use ◽  
The Impact ◽  
Survey Questionnaires

IntroductionOne possible reason for the rapid proliferation of waterpipe (WP) smoking is the pervasive use of flavoured WP tobacco. To begin to understand the impact of WP tobacco flavours, the current study examined the impact of a preferred WP tobacco flavour compared with a non-preferred tobacco flavoured control on user’s smoking behaviour, toxicant exposure and subjective smoking experience.MethodThirty-six current WP smokers completed two, 45-minute ad libitum smoking sessions (preferred flavour vs non-preferred tobacco flavour control) in a randomised cross-over design. Participants completed survey questionnaires assessing subjective smoking experience, exhaled carbon monoxide (eCO) testing, and provided blood samples for monitoring plasma nicotine. WP smoking topography was measured continuously throughout the smoking session.ResultsWhile participants reported an enhanced subjective smoking experience including greater interest in continued use, greater pleasure derived from smoking, increased liking and enjoyment, and willingness to continue use after smoking their preferred WP tobacco flavour (p values <0.05), no significant differences were observed in nicotine and carbon monoxide boost between flavour preparations. Greater average puff volume (p=0.018) was observed during the non-preferred flavour session. While not significant, measures of flow rate, interpuff interval (IPI), and total number of puffs were trending towards significance (p values <0.10), with decreased IPI and greater total number of puffs during the preferred flavour session.DiscussionThe current study is the first to examine flavours in WP smoking by measuring preferred versus control preparations to understand the impact on subjective experience, smoking behaviour and toxicant exposure. The pattern of results suggests that even this relatively minor manipulation resulted in significant changes in subjective experience. These results indicate a possible need for regulations restricting flavours in WP tobacco as with combustible cigarettes.

scholarly journals Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

2021 ◽  
Vol 13 (11) ◽  
pp. 2041
Author(s):  
Lisa Milani ◽  
Norman B. Wood
Keyword(s):  
Water Cycle ◽  
Mean Values ◽  
The Mean ◽  
Satellite Missions ◽  
The Impact ◽  
Global Mean

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

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