The IPCC https://www.ipcc.ch has numerous free-to-download reports with some such material.

A link to IPCC's website: https://www.ipcc.ch .

You can also download other research materials relating to the subject matter on google scholar

Dear Lilian,

I think that a ready-to-use database like WorldClim is not available that contain decadal bioclimatic variables. But if you download monthly temperature and precipitation values in raster format, a bunch of tools are available that can calculate bioclimatic variables from these rastersin R (climates, BioCalc and dismo packages), ANUCLIM, ESRI ArcGIS, GRASS GIS, DIVA-GIS etc.

HZH,

Ákos

Hi the IPCC is the best site to visit: https://www.ipcc.ch/. The last available report : https://www.ipcc.ch/report/ar5/syr/

Data from CMIP5 can be downloaded from: https://esgf-node.llnl.gov/projects/esgf-llnl/

Decadal bioclimatic variables may also be prone to the uncertainty related to the selection of the specific month/quarter (e.g. wettest quarter). This paper might be relevant for you:

Article The way bioclimatic variables are calculated has impact on p...

Climate Change and Water resources. This paper, tries to answer the following question: is it possible to predict Water Resources only with GCMs, without downscaling, and what would be the resulting uncertainties? Besbes M., Chahed J. "Predictability of water resources with global climate models. Case of Northern Tunisia", Published online: 12 June 2023, Les Comptes Rendus. Géoscience. Available on:Article Predictability of water resources with global climate models...

Progress in understanding Climate Change and its effects needs advances in modeling Climate Phenomena. "IPCC Models" "Climate Models" "General Circulation Models", appellations are of no importance. It is in all cases Physics-Driven Models developed within multi-disciplinary scientific teams worldwide to describe the evolution of weather phenomena (at short time scales) and climate phenomena that involve long time-scale processes, more complex to analyze, as part of these phenomena are not yet well understood.

This is why, despite the enormous progress already achieved, the predictability of Climate Models (The Earth System Models, ESMs), are not yet sufficiently accurate. The standard deviations between the different models remain of the same order of magnitude as the mean values and huge biases on regional levels are noticed and well documented in technical and scientific references of each of the models.

This should lead us to admit that more research is needed to improve our knowledge of the driving forces that control the climate in order to build more accurate predictive climate models, as scientists do well for weather prediction

See also:

https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_Limits

On Climate Models: From General Circulation Models (GCMs) and Earth System Models (ESMs). General Circulation Models (GCMs)which are the core of weather forecasting Models appeared in the 1960s with the pioneer's work of Manabe (2021 Nobel Prize in Physics). A fundamental point is that is difficult to speak about GCMs and even less of Climate Models without a minimum review starting from Atmosphere Dynamics Models genesis in the 1960s to the actual Earth System Models (ESMs) that participated in the last "CMIP6". These represent the State-of-art of universal knowledge about Climate and its modeling. The results published in 2021 covers 80 ESMs from as many research teams throughout the world. Nowadays, Climate Science and Modelling have attained an international critic-mass never reached in any other domain.

ESMs include a number of components that try to describe the evolution of intercoupled phenomena that govern Climate Phenomena. To understand how this works, one has to know about the progress achieved and still-opened questions related to Climate Models. Mathematically the resolution of the dynamic and the transport equations of physical quantities on more or less important scales provide accurate predetermination in a relatively short time. This is what meteorologists do to deliver us every day their newsletter. This is what the same meteorologists are trying to do with scientists from all sides to build climate models in the long term, sure inaccurate today, exactly as was the 1960s weather model of Manabe, Nobel Prize in Physics 2021, the pioneer of general circulation modeling. The very first general circulation models were based on atmosphere-only physical models (Manabe et al., 1965, Nobel Prize in Physics, 2021), which were quickly improved to take into account the hydrologic cycle and its role in the general circulation of the atmosphere (Smagorinsky et al. 1965). From there, climate modeling has made considerable progress by gradually integrating the many positive or negative feedback processes that occur at different scales between the different components of the system: ocean circulation (Manabe and Bryan, 1969), land hydrological processes (Sellers et al., 1986), sea ice dynamics (Meehl and Washington, 1995), and aerosols (Takemura et al., 2000), biophysical and biogeochemical processes (Cox et al., 2000). Models with these latter components are often called Earth System Models (ESMs) and more recent such models include land and ocean carbon cycle, atmospheric chemistry, dynamic vegetation, and other biogeochemical cycles (Watanabe et al., 2011, Collins et al., 2011). It should be noted that as a whole and for the same reasons, the horns of ESMs, which are based on physical formulations similar to those employed in general circulation models applied in meteorology, have not evolved much, except for the increase in the resolution of the calculations made possible thanks to the increase in the computing capacity or their capacity to assimilate increasingly abundant and precise data; in particular global satellite data, which complements and connects measurements on the ground or at low altitude.

Manabe, S., Smagorinsky, J., & Strickler, R. F. (1965). Simulated climatology of a general circulation model with a hydrologic cycle. Monthly Weather Review, 93(12), 769-798.

Smagorinsky, S. Manabe, and J. L. Holloway, “Numericd Results From a Nine-Level General Circulation Model of the Atmosphere,” Monthly Weather Review, vol. 93, No. 12, Dec. 1965, pp. 727-768.

Manabe, S., & Bryan, K. (1969). Climate calculations with a combined ocean-atmosphere model. J. Atmos. Sci, 26(4), 786-789.

Sellers, P. J., Mintz, Y. C. S. Y., Sud, Y. E. A., & Dalcher, A. (1986). A simple biosphere model (SiB) for use within general circulation models. Journal of the atmospheric sciences, 43(6), 505-531.

Meehl, G. A., & Washington, W. M. (1995). Cloud albedo feedback and the super greenhouse effect in a global coupled GCM. Climate dynamics, 11(7), 399-411.

Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., & Nakajima, T. (2000). Global three‐dimensional simulation of aerosol optical thickness distribution of various origins. Journal of Geophysical Research: Atmospheres, 105(D14), 17853-17873.

Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., & Totterdell, I. J. (2000). Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408(6809), 184-187.

Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., ... & Kawamiya, M. (2011). MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4(4), 845-872.

Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., ... & Woodward, S. (2011). Development and evaluation of an Earth-System model–HadGEM2. Geoscientific Model Development, 4(4), 1051-1075.

See Also:

Besbes, M., & Chahed, J. (2023). Predictability of water resources with global climate models. Case of Northern Tunisia. Comptes Rendus. Géoscience, 355(S1), 1-22. Available on:

Article Predictability of water resources with global climate models...

https://www.researchgate.net/post/Climate_Change_and_Climate_Models_Progress_and_Limits