Causes of differences in model and satellite tropospheric warming rates

In the early twenty-first century, satellite-derived tropospheric warming trends were generally smaller than trends estimated from a large multi-model ensemble. Because observations and coupled model simulations do not have the same phasing of natural internal variability, such decadal differences in simulated and observed warming rates invariably occur. Here we analyse global-mean tropospheric temperatures from satellites and climate model simulations to examine whether warming rate differences over the satellite era can be explained by internal climate variability alone. We find that in the last two decades of the twentieth century, differences between modelled and observed tropospheric temperature trends are broadly consistent with internal variability. Over most of the early twenty-first century, however, model tropospheric warming is substantially larger than observed; warming rate differences are generally outside the range of trends arising from internal variability. The probability that multi-decadal internal variability fully explains the asymmetry between the late twentieth and early twenty-first century results is low (between zero and about 9%). It is also unlikely that this asymmetry is due to the combined effects of internal variability and a model error in climate sensitivity. We conclude that model overestimation of tropospheric warming in the early twenty-first century is partly due to systematic deficiencies in some of the post-2000 external forcings used in the model simulations

State of the climate in 2018

In 2018, the dominant greenhouse gases released into Earth’s atmosphere—carbon dioxide, methane, and nitrous oxide—continued their increase. The annual global average carbon dioxide concentration at Earth’s surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year’s end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981–2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June’s Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°–0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000–18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981–2010 average of 82. Eleven tropical cyclones reached Saffir–Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael’s landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and$6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14–15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000–10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars)

Reconciling tropospheric temperature trends from the microwave sounding unit

Thesis (Master's)--University of Washington, 2012The University of Alabama at Huntsville (UAH), Remote Sensing Systems (RSS), and the National Oceanic and Atmospheric Administration (NOAA) have constructed long-term temperature records for deep atmospheric layers using satellite microwave sounding unit (MSU) and advanced microwave sounding unit (AMSU) observations. However, these groups disagree on the magnitude of global temperature trends since 1979, including the trend for the mid-tropospheric layer (TMT). This study evaluates the selection of the MSU TMT warm target factor for the NOAA-9 satellite using five homogenized radiosonde products as references. The analysis reveals that the UAH TMT product has a positive bias of 0.051 ± 0.031 in the warm target factor that artificially reduces the global TMT trend by 0.042 K decade−1 for 1979 − 2009. Accounting for this bias, we estimate that the global UAH TMT trend should increase from 0.038 K decade−1 to 0.080 K decade−1, effectively eliminating the trend difference between UAH and RSS and decreasing the trend difference between UAH and NOAA by 47%. This warm target factor bias directly affects the UAH lower tropospheric (TLT) product and tropospheric temperature trends derived from a combination of TMT and lower stratospheric (TLS) channels

On the structure of atmospheric warming in models and observations: Implications for the lapse rate feedback

Thesis (Ph.D.)--University of Washington, 2016-12This dissertation investigates the structure of atmospheric warming in observations and general circulation models (GCMs). Theory and GCMs suggest that warming is amplified in the tropical upper troposphere relative to the lower troposphere and the surface -- a phenomenon known as vertical amplification. We assess model and observational agreement using several amplification metrics derived from the satellite-borne microwave sounding unit (MSU) atmospheric temperature trends. An important correction to the satellite microwave record is the removal of temperature drifts caused by changes in diurnal sampling. This correction the principal source of uncertainty in microwave temperature datasets. Furthermore, in three existing datasets, the ratio of tropical warming between the upper troposphere (T24 channel) and the surface (dT24/dTs ~ 0.6 -- 1.3) is lower than that of GCMs (~1.4 -- 1.6). To better understand these issues, we produced an alternate MSU dataset with an improved diurnal correction. We show that existing MSU datasets likely underestimate tropical mid-tropospheric temperature trends. Subsequent improvements to MSU datasets using similar diurnal correction techniques leads to amplification ratios (between T24 and the surface) that are in accord with models. Another measure of tropical tropospheric amplification is the relative warming between the upper troposphere (T24) and the lower-middle troposphere (TLT). We show that most GCMs have excessive T24/TLT amplification compared to satellite microwave observations, even when models are forced with prescribed sea-surface temperatures (SSTs). A number of possible reasons for this discrepancy are assessed. Observational uncertainty in the satellite microwave record is substantial and, when taken into account, many models agree with observations within the observational uncertainty range, though about half of the model ensemble members considered still have significant discrepancies compared to observations. Our findings indicate that the prescribed ozone and stratospheric aerosol forcings do not effect T24/TLT amplification in models. On the other hand, model parameterizations for convection and microphysics and, to a lesser degree, uncertainty in the prescribed SST dataset can influence model amplification behavior and bring models into closer accord with observations. In all, significant T24/TLT discrepancies between models and observations remain, but may be reduced with improved model parameterizations. An underlying motivation for understanding the structure of atmospheric warming is that it is responsible for a large negative lapse rate feedback in future climate simulations. To understand factors that control the global lapse rate feedback across models, we use principal component analysis to find the modes of variability that best explain variance in the local lapse rate feedback. We find that models exhibit marked variability in the lapse rate feedback in the southern hemisphere extratropics. This mode is strongly correlated with the global average lapse rate feedback and is largely a function of the competing influence of tropical and Antarctic surface warming. We show that muted southern ocean sea surface warming and the non-local influence of tropical surface warming contributes to a highly variable lapse rate feedback in the sub-Antarctic across models. This behavior is dissimilar to northern hemisphere high latitudes, which are characterized by strong Arctic amplification and a relatively uniform local lapse rate feedback across GCMs. Climatological Antarctic sea ice extent influences Antarctic warming and, as a result, influences both the meridional profile of warming in the southern hemisphere and the global lapse rate feedback

Using particle induced x-ray analysis and plasma mass spectrometry to Investigate mercury emissions from anthropogenic sources

Inductively Coupled Plasma Mass Spectrometry, ICP-MS, were used to investigate mercury pollution in Norway Spruce tree bark (Picea abies) located downwind from the Huntley coal-fired power plant in North Tonawanda, New York. We predicted that mercury concentration, C, would fall inversely with distance, r, and experimentally determined that after a 5.77 km – 9.47 km deposition delay, mercury concentration fell as C ∝r-1.48 with an r2 value of 0.96. PIXE was useful in quickly and non-destructively (experiment could be done repeatedly) determining order of magnitude concentrations for heavy metals with concentrations less than one part-per-million. ICP-MS requires a more involved sample preparation method, but was useful in very precisely determining mercury concentrations. After comparing PIXE to ICP-MS, we found that the two methods were in agreement, although with varying precision. For the comparison sample, we found a PIXE value of 100 ± 70 μg kg-1 compared to 43 ± 1 μg kg-1 for ICPMS (mass of mercury per mass of bark). Using the experimental concentration – distance relationship, we found that the Huntley Power Plant had a sphere of influence (mercury concentration above background levels) of 100 – 200 km. Department of Physics and Astronomy, June 2008

Discrepancies in tropical upper tropospheric warming between atmospheric circulation models and satellites

Recent studies have examined tropical upper tropospheric warming by comparing coupled atmosphere–ocean global circulation model (GCM) simulations from Phase 3 of the Coupled Model Intercomparison Project (CMIP3) with satellite and radiosonde observations of warming in the tropical upper troposphere relative to the lower-middle troposphere. These studies showed that models tended to overestimate increases in static stability between the upper and lower-middle troposphere. We revisit this issue using atmospheric GCMs with prescribed historical sea surface temperatures (SSTs) and coupled atmosphere–ocean GCMs that participated in the latest model intercomparison project, CMIP5. It is demonstrated that even with historical SSTs as a boundary condition, most atmospheric models exhibit excessive tropical upper tropospheric warming relative to the lower-middle troposphere as compared with satellite-borne microwave sounding unit measurements. It is also shown that the results from CMIP5 coupled atmosphere–ocean GCMs are similar to findings from CMIP3 coupled GCMs. The apparent model-observational difference for tropical upper tropospheric warming represents an important problem, but it is not clear whether the difference is a result of common biases in GCMs, biases in observational datasets, or both

Blue carbon potential of eelgrass in the Puget Sound

Coastal ecosystems sequester and store large amounts of carbon making them important contributors to the global carbon budget. There are growing efforts to quantify this coastal marine carbon, referred to as blue carbon, in different regions of the world. Due to the diversity of species and habitats worldwide, and to high spatial variability in carbon storage capacity, local estimates yield the best measures of stored carbon. In this study we estimate the blue carbon potential of eelgrass ecosystems in the Puget Sound, WA, USA. Although direct measures of sediment carbon in seagrass beds are mostly unavailable for the Puget Sound, we used carbon storage values from Padilla Bay, the region with the largest eelgrass extent, and extrapolated carbon storage capacity of Puget Sound eelgrass beds. Our analysis suggests that eelgrass beds in Puget Sound sequester carbon at a rate of 4.2 ± 1.9 ktC yr-1 and store 1819 ± 239 ktC of carbon in the sediment. The uncertainties associated with these estimates can be reduced through location-specific studies of the effects of depth, eutrophication, and sedimentation on carbon burial and storage

xCDAT/xcdat: v0.4.0

<p>v0.4.0 (9 November 2022)</p> <p>This minor release includes a feature update to support datasets that have <em>N</em> dimensions mapped to <em>N</em> coordinates to represent an axis. This means <code>xcdat</code> APIs are able to intelligently select which axis's coordinates and bounds to work with if multiple are present within the dataset. Decoding time is now a lazy operation, leading to significant upfront runtime improvements when opening datasets with <code>decode_times=True</code>.</p> <p>A new notebook called "A Gentle Introduction to xCDAT" was added to the documentation gallery to help guide new xarray/xcdat users. xCDAT is now hosted on Zenodo with a DOI for citations.</p> <p>There are various bug fixes for bounds, naming of spatial weights, and a missing flag for <code>xesmf</code> that broke curvilinear regridding.</p> <p>Features</p> <ul> <li>Support for N axis dimensions mapped to N coordinates by @tomvothecoder and @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/343">https://github.com/xCDAT/xcdat/pull/343</a> <ul> <li>Rename <code>get_axis_coord()</code> to <code>get_dim_coords()</code> and <code>get_axis_dim()</code> to <code>get_dim_keys()</code></li> <li>Update spatial and temporal accessor class methods to refer to the dimension coordinate variable on the data_var being operated on, rather than the parent dataset</li> </ul> </li> <li>Decoding times (<code>decode_time()</code>) is now a lazy operation, which results in significant runtime improvements by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/343">https://github.com/xCDAT/xcdat/pull/343</a></li> </ul> <p>Bug Fixes</p> <ul> <li>Fix <code>add_bounds()</code> not ignoring 0-dim singleton coords by @tomvothecoder and @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/343">https://github.com/xCDAT/xcdat/pull/343</a></li> <li>Fix name of spatial weights with singleton coord by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/379">https://github.com/xCDAT/xcdat/pull/379</a></li> <li>Fixes <code>xesmf</code> flag that was missing which broke curvilinear regridding by @jasonb5 and @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/374">https://github.com/xCDAT/xcdat/pull/374</a></li> </ul> <p>Documentation</p> <ul> <li>Add FAQs section for temporal metadata by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/383">https://github.com/xCDAT/xcdat/pull/383</a></li> <li>Add gentle introduction notebook by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/373">https://github.com/xCDAT/xcdat/pull/373</a></li> <li>Link repo to Zenodo and upload GitHub releases by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/367">https://github.com/xCDAT/xcdat/pull/367</a></li> <li>Update project overview, FAQs, and add a link to xarray tutorials by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/365">https://github.com/xCDAT/xcdat/pull/365</a></li> <li>Update feature list, add metadata interpretation to FAQs, and add <code>ipython</code> syntax highlighting for notebooks by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/362">https://github.com/xCDAT/xcdat/pull/362</a></li> </ul> <p>DevOps</p> <ul> <li>Update release-drafter template by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/371">https://github.com/xCDAT/xcdat/pull/371</a> and <a href="https://github.com/xCDAT/xcdat/pull/370">https://github.com/xCDAT/xcdat/pull/370</a></li> <li>Automate release notes generation by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/368">https://github.com/xCDAT/xcdat/pull/368</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/xCDAT/xcdat/compare/v0.3.3...v0.4.0">https://github.com/xCDAT/xcdat/compare/v0.3.3...v0.4.0</a></p&gt

xCDAT/xcdat: v0.6.0rc1

<p>v0.6.0rc1 (7 July 2023)</p> <p>This is the first release candidate for the upcoming v0.6.0 release. This version is intended to be used for testing new features, improvements, and bug fixes. Refer to the changelog below for more information.</p> <p>Features</p> <ul> <li>Functions to produce accurate time bounds by @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/418">https://github.com/xCDAT/xcdat/pull/418</a></li> <li>Add API extending xgcm vertical regridding by @jasonb5 in <a href="https://github.com/xCDAT/xcdat/pull/388">https://github.com/xCDAT/xcdat/pull/388</a></li> <li>Update <code>create_grid</code> args to improve usability by @jasonb5 in <a href="https://github.com/xCDAT/xcdat/pull/507">https://github.com/xCDAT/xcdat/pull/507</a></li> </ul> <p>Bug Fixes</p> <ul> <li>Improves error when axis is missing/incorrect attributes by @jasonb5 in <a href="https://github.com/xCDAT/xcdat/pull/481">https://github.com/xCDAT/xcdat/pull/481</a></li> <li>Fix multi-file dataset spatial average orientation and weights when lon bounds span prime meridian by @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/495">https://github.com/xCDAT/xcdat/pull/495</a></li> <li>Fixes preserving ds/da attributes in the regrid2 module by @jasonb5 in <a href="https://github.com/xCDAT/xcdat/pull/468">https://github.com/xCDAT/xcdat/pull/468</a></li> </ul> <p>Deprecations</p> <ul> <li>Add deprecation warning for CDML/XML support in <code>open_mfdataset()</code> by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/503">https://github.com/xCDAT/xcdat/pull/503</a></li> </ul> <p>Documentation</p> <ul> <li>Typo fix for doc by @lee1043 in <a href="https://github.com/xCDAT/xcdat/pull/491">https://github.com/xCDAT/xcdat/pull/491</a></li> <li>Update documentation in regrid2.py by @lee1043 in <a href="https://github.com/xCDAT/xcdat/pull/509">https://github.com/xCDAT/xcdat/pull/509</a></li> <li>Remove cdms-filemap references in API docstrings by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/504">https://github.com/xCDAT/xcdat/pull/504</a></li> <li>Add more fields to GH Discussions question form by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/480">https://github.com/xCDAT/xcdat/pull/480</a></li> <li>Add Q&A GH discussions template by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/479">https://github.com/xCDAT/xcdat/pull/479</a></li> <li>Update FAQs question covering datasets with conflicting bounds by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/474">https://github.com/xCDAT/xcdat/pull/474</a></li> <li>Functions to produce accurate time bounds by @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/418">https://github.com/xCDAT/xcdat/pull/418</a></li> <li>Add Google Groups mailing list to docs by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/452">https://github.com/xCDAT/xcdat/pull/452</a></li> <li>Fix README link to CODE-OF-CONDUCT.rst by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/444">https://github.com/xCDAT/xcdat/pull/444</a></li> <li>Replace LLNL E3SM License with xCDAT License by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/443">https://github.com/xCDAT/xcdat/pull/443</a></li> </ul> <p>DevOps</p> <ul> <li>Fix Python deprecation comment in conda env yml files by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/514">https://github.com/xCDAT/xcdat/pull/514</a></li> <li>Simplify conda environments and move configs to <code>pyproject.toml</code> by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/512">https://github.com/xCDAT/xcdat/pull/512</a></li> <li>Update DevOps to cache conda and fix attributes not being preserved with <code>xarray > 2023.3.0</code> by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/465">https://github.com/xCDAT/xcdat/pull/465</a></li> <li>Update GH Actions to use <code>mamba</code> by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/450">https://github.com/xCDAT/xcdat/pull/450</a></li> <li>Bump to 0.6.0rc1 by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/515">https://github.com/xCDAT/xcdat/pull/515</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/xCDAT/xcdat/compare/v0.5.0...v0.6.0rc1">https://github.com/xCDAT/xcdat/compare/v0.5.0...v0.6.0rc1</a></p&gt

xCDAT/xcdat: v0.5.0

<p>v0.5.0 (27 March 2023)</p> <p>This long-awaited minor release includes feature updates to support an optional user-specified climatology reference period when calculating climatologies and departures, support for opening datasets using the <code>directory</code> key of the legacy CDAT <a href="https://cdms.readthedocs.io/en/latest/manual/cdms_6.html">Climate Data Markup Language (CDML)</a> format (an XML dialect), and improved support for using custom time coordinates in temporal APIs.</p> <p>This release also includes a bug fix for singleton coordinates breaking the <code>swap_lon_axis()</code> function. Additionally, Jupyter Notebooks for presentations and demos have been added to the documentation.</p> <p>Features</p> <ul> <li>Update departures and climatology APIs with reference period by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/417">https://github.com/xCDAT/xcdat/pull/417</a></li> <li>Wrap open_dataset and open_mfdataset to flexibly open datasets by @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/385">https://github.com/xCDAT/xcdat/pull/385</a></li> <li>Add better support for using custom time coordinates in temporal APIs by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/415">https://github.com/xCDAT/xcdat/pull/415</a></li> </ul> <p>Bug Fixes</p> <ul> <li>[Doc]: Fix missing <code>xesmf</code> APIs and update slides in intro notebook by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/428">https://github.com/xCDAT/xcdat/pull/428</a></li> <li>Raise warning if no time coords found with <code>decode_times</code> by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/409">https://github.com/xCDAT/xcdat/pull/409</a></li> <li>Bump conda env dependencies by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/408">https://github.com/xCDAT/xcdat/pull/408</a></li> <li>Fix <code>swap_lon_axis()</code> breaking when sorting with singleton coords by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/392">https://github.com/xCDAT/xcdat/pull/392</a></li> </ul> <p>Documentation</p> <ul> <li>Bump to v0.5.0 by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/436">https://github.com/xCDAT/xcdat/pull/436</a></li> <li>Update xsearch-xcdat-example.ipynb by @pochedls in <a href="https://github.com/xCDAT/xcdat/pull/425">https://github.com/xCDAT/xcdat/pull/425</a></li> <li>Updates xesmf docs by @jasonb5 in <a href="https://github.com/xCDAT/xcdat/pull/432">https://github.com/xCDAT/xcdat/pull/432</a></li> <li>[Doc]: Fix missing <code>xesmf</code> APIs and update slides in intro notebook by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/428">https://github.com/xCDAT/xcdat/pull/428</a></li> <li>Add presentations and demos to sphinx toctree by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/422">https://github.com/xCDAT/xcdat/pull/422</a></li> <li>[Doc]: Add <code>xsearch</code> demo notebook for CWSS Presentation by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/398">https://github.com/xCDAT/xcdat/pull/398</a></li> <li>Update temporal <code>.average</code> and <code>.departures</code> docstrings by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/407">https://github.com/xCDAT/xcdat/pull/407</a></li> </ul> <p>DevOps</p> <ul> <li>Bump conda env dependencies by @tomvothecoder in <a href="https://github.com/xCDAT/xcdat/pull/408">https://github.com/xCDAT/xcdat/pull/408</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/xCDAT/xcdat/compare/v0.4.0...0.5.0">https://github.com/xCDAT/xcdat/compare/v0.4.0...0.5.0</a></p&gt