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Data assimilation is an increasingly popular technique in Mars atmospheric science, but its effect on the mean states of the underlying atmosphere models has not been thoroughly examined. The robustness of results to the choice of model and assimilation algorithm also warrants further study. We investigate these issues using two Mars general circulation models (MGCMs), with particular emphasis on zonal wind and temperature fields. When temperature retrievals from the Mars Global Surveyor Thermal Emission Spectrometer (TES) are assimilated into the U.K.‐Laboratoire de Météorologie Dynamique MGCM to create the Mars Analysis Correction Data Assimilation (MACDA) reanalysis, low‐level zonal jets in the winter northern hemisphere shift equatorward and weaken relative to a free‐running control simulation from the same MGCM. The Ensemble Mars Atmosphere Reanalysis System (EMARS) reanalysis, which is also based on TES temperature retrievals, also shows jet weakening (but less if any shifting) relative to a control simulation performed with the underlying Geophysical Fluid Dynamics Laboratory MGCM. Examining higher levels of the atmosphere, monthly mean three‐dimensional temperature and zonal wind fields are in generally better agreement between the two reanalyses than between the two control simulations. In conjunction with information about the MGCMs' physical parameterizations, intercomparisons between the various reanalyses and control simulations suggest that overall the EMARS control run is plausibly less biased (relative to the true state of the Martian atmosphere) than the MACDA control run. Implications for future observational studies are discussed. Plain Language Summary: An increasingly popular way to study Martian weather and climate is to combine atmospheric temperature observations with a computer model (specifically, a global climate model). The process of combining model and observations is called "data assimilation," and the resulting merged data set is called a "reanalysis." One advantage of reanalyses is that they include variables (such as wind) that are not directly observed. For scientific and practical applications, we want these variables to be reasonably accurate—however, it is not clear how well data assimilation algorithms compute them. Our study investigates this issue using two Mars reanalyses and two model simulations that do not assimilate temperature data. We focus on slowly varying atmospheric phenomena (timescales from 10 Mars days to a season). Assimilating temperature data into two different global climate models changes the strength and/or spatial pattern of east‐west winds at low altitudes. Furthermore, monthly mean three‐dimensional temperature and east‐west wind fields agree better between reanalyses than between non‐assimilating model simulations. This suggests that the data assimilation process is basically successful. One non‐assimilating model simulation has less realistic representations of atmospheric physical processes than the other—we argue that this plausibly gives it larger biases relative to the true state of the atmosphere. Key Points: Assimilating temperature data in U.K.‐Laboratoire de Météorologie Dynamique Mars climate model weakens, shifts northern winter low‐level jet, but has less effect on Geophysical Fluid Dynamics Laboratory modelTime mean flows generally agree better in the Mars Analysis Correction Data Assimilation (MACDA) and Ensemble Mars Atmosphere Reanalysis System (EMARS) reanalyses than in their associated control runsReanalysis‐control run mean state differences suggest that the EMARS control run has smaller biases than the MACDA control run [ABSTRACT FROM AUTHOR]

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations. [ABSTRACT FROM AUTHOR]

Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability and Mars thermal tides, the Geophysical Fluid Dynamics Laboratory Mars Global Climate Model with the 4D-local ensemble transform Kalman filter (4D-LETKF) is used to perform an analysis assimilating spacecraft temperature retrievals. We find that the use of a 'traditional' 6-hr assimilation cycle induces spurious forcing of a resonantly enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a 'topographic' stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated 'true' tides even in the absence of a radiatively active water ice cloud parameterisation. In order to compare the performance of different assimilation window lengths, short-term to mid-range forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during Northern Hemisphere summer, it is not the assimilation window length, but the radiatively active water ice clouds that influence the model prediction. A 'diurnal bias correction' that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences between forecasts and observations, compensate for the absence of water ice cloud parameterisation and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are discussed. [ABSTRACT FROM AUTHOR]

Abstract: The local ensemble transform Kalman filter (LETKF) is applied to the GFDL Mars general circulation model (MGCM) to demonstrate the potential benefit of an advanced data assimilation method. In perfect model (aka identical twin) experiments, simulated observations are used to assess the performance of the LETKF–MGCM system and to determine the dependence of the assimilation on observational data coverage. Temperature retrievals are simulated at locations that mirror the spatial distribution of the Thermal Emission Spectrometer (TES) retrievals from the Mars Global Surveyor (MGS). The LETKF converges quickly and substantially reduces the analysis and subsequent forecast errors in both temperature and velocity fields, even though only temperature observations are assimilated. The LETKF is also found to accurately estimate the magnitude of forecast uncertainties, notably those associated with the phase and amplitude of baroclinic waves along the boundary of the polar ice cap during Northern Hemisphere winter. [ABSTRACT FROM AUTHOR]