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Summary
Observation-model consistency in climate studies refers to how closely real-world climate observations match simulated results from climate models, helping researchers understand if models can accurately predict climate trends. This process is crucial for improving climate forecasts and identifying reasons for discrepancies, such as variability in natural systems, model limitations, or gaps in observational data.
Compare carefully: When evaluating climate model outputs, ensure you are using consistent methods and up-to-date observational datasets to avoid mismatched conclusions.
Identify mismatches: Pay attention to regions or time periods where observations and models disagree, as these can highlight areas for further research and improvements.
Seek improvements: Support ongoing monitoring and refinement of climate models and observational tools to boost confidence in projections and understand uncertainties.
Can climate models reproduce observed trends?
The answer can be challenging. Our new review paper in Science Advances led by Isla Simpson and Tiffany Shaw discusses challenges and ways forward in confronting climate models and observations.
It's tricky. Climate models and observations may disagree (1) by chance, due to unforced internal variability, (2) due to error in the model response, (3) due to inaccurate prescribed external forcings, (4) due to incomplete or uncertain observations or (5) due to inappropriate comparison methods.
The paper discusses ways forward in disentangling the reasons for potential mismatches between observed and simulated trends.
It provides a long catalogue of examples of success, discrepancies and unclear situations that require further attention.
https://lnkd.in/dHrEJfDh
Let by Isla Simpson and Tiffany Shaw with Paulo Ceppi, Amy Clement, Erich Fischer, Kevin Grise, Angeline Pendergrass, James Screen, Robert Jinglin Wills, Tim Woollings, Russell Blackport, Joonsuk Kang, and Stephen Po-Chedley supported by US CLIVAR
—🖇️ Trusting projections of the Atlantic Meridional Ocean Circulation (AMOC) weakening is difficult because of significant discrepancies between observational data and climate model outputs, especially regarding the AMOC's historical trend:
While the IPCC reports have indicated a weakening, the confidence in this finding has varied, and recent climate models (CMIP6) have shown a slight strengthening over historical periods, contrary to some proxy data suggesting a decline…
This observation-model discrepancy, along with the large natural variability of the AMOC and challenges in calibration, makes future AMOC projections uncertain and highlights the need for more direct observations and improved models, such as those from current monitoring programs.
https://lnkd.in/gXDqSxSUhttps://lnkd.in/geMNxN7ahttps://lnkd.in/gd3BiAWMhttps://lnkd.in/gSBwRq6q
What are two of the biggest uncertainties in climate?
On the top of my list are aerosols and clouds. So I was very surprised to realize that climate models tend to agree with each other when it comes to trends in the amount of sunlight reaching the land surface -- since this is largely a function of reflection, absorption, or scattering related to atmospheric water vapor and aerosols*. In general, CMIP6 climate models brighten in the Eastern US and Europe (reduced aerosols), dim in India/China, and don't do much elsewhere (see CMIP6 panel in figure below).
*Caveat: most CMIP6 models use prescribed aerosol properties and the same historical inventory, so the model spread is unlikely to represent true uncertainty.
The observations (as measured by ERA5 -- we do our best to validate it in the paper, open access, linked below) look pretty different, including greater brightening in the central/west US than eastern US, and substantial increases in downward shortwave throughout South America (see ERA5 panel below).
To understand whether the observed trends are distinct from the multimodel mean (which averages out internal variability) but consistent with the range of outcomes simulated by our climate models, we comprehensively compared the observations to 237 different CMIP6-era simulations. In multiple regions of the world, including the US Southwest, parts of South America, and eastern China, the trends in ERA5 are consistently at the edge or outside the ensemble. This is shown in the righthand panel below, in which the ERA5 trends are ranked within the climate model ensemble. A rank of 238 means that the ERA5 trends exceed all the model trends.
Why? We don't totally know at the moment. In the GRL paper where Isla Simpson and I explore these trends (https://lnkd.in/gubYUrXH), we find that differences in cloud trends is the likely (and expected) driver. But this raises the usual question: is the model-observational discrepancy in trends due to model errors in the forced response or deficiencies in simulation of internal variability? The role of errors in ERA5 cannot be discounted either, since sources of "ground truth" are limited before the 2000s.
Intriguingly, many although not all of the regions with the newly identified model-observational discrepancy in downward shortwave radiation are also those with a discrepancy in humidity trends (https://lnkd.in/gSPhpdjP).
As always, there is more to do, including a deeper dive into the trends in continental cloudiness. My personal hypothesis is that all of these discrepancies (clouds, radiation, humidity) are linked to model-observational differences in land-atmosphere interactions. Would love to hear thoughts in the comments or by email!