{"id":385343,"date":"2025-06-27T08:52:30","date_gmt":"2025-06-27T06:52:30","guid":{"rendered":"https:\/\/climatescience.press\/?p=385343"},"modified":"2025-06-27T08:52:31","modified_gmt":"2025-06-27T06:52:31","slug":"scafetta-climate-models-have-issues","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=385343","title":{"rendered":"Scafetta: Climate Models Have\u00a0Issues"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Science Matters<\/a><\/p>\n\n\n\n

By\u00a0Ron Clutz<\/a><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

On June 18, 2025 Nicola Scafetta published Detection, attribution, and modeling of climate change:  key open issues.<\/strong><\/a>  Excerpts in italics with my bolds and added images.<\/p>\n\n\n\n

Abstract<\/strong><\/em><\/p>\n\n\n\n

The Coupled Model Intercomparison Project (CMIP) global climate models (GCMs) assess<\/strong> that nearly 100% of global surface warming<\/strong> observed between 1850\u20131900 and 2011\u20132020 is attributable to<\/strong> anthropogenic drivers like greenhouse gas emissions.<\/strong> These models also generate future climate projections based on shared socioeconomic pathways (SSPs)<\/strong>, aiding in risk assessment and the development of costly \u201cNet-Zero\u201d climate mitigation strategies.<\/em><\/p>\n\n\n

\n
\"\"
Figure 1. Anthropgenic and natural contributions. (a) Locked scaling factors, weak Pre Industrial Climate Anomalies (PCA). (b) Free scaling, strong PCA Source:\u00a0Larminat, P. de (2023)<\/strong><\/a><\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

Yet, as this study discusses, the CMIP GCMs face\u00a0significant scientific challenges<\/strong>\u00a0in attributing and modeling climate change, particularly in\u00a0capturing natural climate variability<\/strong>\u00a0over multiple timescales throughout the Holocene. Other key concerns include the\u00a0reliability of global surface temperature records,\u00a0<\/strong>the accuracy of\u00a0solar irradiance models,<\/strong>\u00a0and the robustness of\u00a0climate sensitivity estimates.<\/strong>\u00a0Global warming estimates may be overstated due to uncorrected non-climatic biases, and the GCMs may significantly underestimate solar and astronomical influences on climate variations.<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The\u00a0equilibrium climate sensitivity (ECS)<\/strong>\u00a0to radiative forcing could be lower than commonly assumed;\u00a0empirical<\/strong>\u00a0findings suggest ECS\u00a0values lower than 3\u00b0C and possibly even closer to 1.1 \u00b1 0.4 \u00b0C.<\/strong>\u00a0Empirical models incorporating natural variability suggest that the 21st-century global\u00a0warming may remain moderate, even under SSP scenarios that do not necessitate Net-Zero emission policies.<\/strong><\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

These findings raise important questions regarding the necessity and urgency of implementing aggressive climate mitigation strategies. While GCMs<\/strong> remain essential tools for climate research and policymaking, their scientific limitations<\/strong> underscore the need<\/strong> for more refined modeling approaches to ensure accurate future climate assessments. Addressing uncertainties related to climate change detection, natural variability, solar influences, and climate sensitivity to<\/strong> radiative forcing will enhance predictions and better inform sustainable climate strategies.<\/strong><\/em><\/p>\n\n\n\n

Discussion<\/strong><\/em><\/h4>\n\n\n\n

Scientific challenges in climate detection, attribution, and modeling stem from three primary issues:<\/em><\/p>\n\n\n\n

1. the inherent uncertainty of<\/strong> what measurements<\/strong> really indicate complicates the detection of climate change and its causative factors;<\/em>
2. the anthropogenic<\/strong> contribution is superimposed<\/strong> to<\/strong> natural climate variability<\/strong>, necessitating comprehensive understanding and accurate modeling of the latter;<\/em>
3. key physical processes,<\/strong> such as cloud formation and solar contributions to climate dynamics, remain poorly characterized.<\/strong><\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure 1:<\/em><\/strong><\/p>\n\n\n\n

(A) Compilation of the radiative forcing functions utilized in the CMIP5 GCMs (adapted from IPCC,2013, Figure 8.18).<\/em>
(B) Variations in observed global surface temperature (black) alongside the CMIP3 and CMIP5 model simulations incorporating only natural forcing and combined natural-anthropogenic forcing (adapted from IPCC, 2013, FAQ 10.1, Figure 1).<\/em>
(C) Compilation of the radiative forcing functions utilized in the CMIP6 GCMs (adapted from IPCC, 2021, Figure 2.10).<\/em>
(D) Observed global surface temperature variations (black) alongside the CMIP6 model simulations incorporating only natural forcing and combined naturalanthropogenic forcing (adapted from IPCC, 2021, Figure SPM.1).<\/em><\/p>\n\n\n\n

Notably, in both (B) and (D), the observational data necessary
to validate the GCM predictions that consider only natural forcings
are not reported because they do not exist.<\/strong><\/em><\/p>\n\n\n\n

While all available GCMs indicate that the positive feedbacks surpass the negative ones<\/strong> thus amplifying the effects of radiative forcing, large uncertainties<\/strong> associated with crucial feedback mechanisms \u2014 particularly those related to water vapor and cloud formation \u2014 remain substantial.<\/strong><\/em><\/p>\n\n\n\n

Feedback mechanisms include:<\/strong><\/em><\/p>\n\n\n\n

\u2022 Water Vapor<\/strong> Feedback \u2014 A positive feedback governed by the Clausius-Clapeyron law, which links ocean vaporation rates to temperature increases;<\/em>
\u2022 Albedo<\/strong> Feedback \u2014 A positive feedback arising from changes in surface reflectivity due to ice and snow<\/em>
cover variations;<\/em>
\u2022 Cloud<\/strong> Feedback \u2014 Particularly challenging to quantify, as cloud formation, type, and distribution are sensitive to warming; certain clouds cool the surface by reflecting solar radiation, while others trap emitted<\/em>
heat, making their net contribution highly uncertain;<\/em>
\u2022 Lapse Rate<\/strong> Feedback \u2014 A negative feedback involving modifications to atmospheric temperature vertical<\/em>
gradients;<\/em>
\u2022 Carbon Cycle<\/strong> Feedback \u2014 Activated by warming-induced CO2 release from soils and oceans (per Henry\u2019s law), further increasing atmospheric CO2 concentrations;<\/em>
\u2022 Vegetation<\/strong> Feedback \u2014 Temperature and precipitation changes alter vegetation cover, which influences<\/em>
carbon storage and surface albedo.<\/em><\/p>\n\n\n\n

The CMIP6 GCMs are also employed to simulate future climate scenarios based on hypothetical radiative <\/strong>forcing <\/strong>functions derived from Shared Socioeconomic Pathways (SSPs)<\/strong>. The ones mainly adopted in the IPCC AR6 are:<\/em>
\u2022 SSP1-2.6<\/strong> \u2014 low greenhouse gas emissions, with robust adaptation and mitigation measures leading to<\/em>
Net-Zero CO2 emissions between 2050\u20132075;<\/em>
\u2022 SSP2-4.5<\/strong> \u2014 intermediate emissions, where CO2 levels remain near current levels until 2050 and subsequently decline without achieving Net-Zero by 2100;<\/em>
\u2022 SSP3-7.0<\/strong> \u2014 high emissions, with CO2 concentrations doubling by 2100 under minimal policyintervention;<\/em>
\u2022 SSP5-8.5<\/strong> \u2014 very high emissions, with CO2 levels tripling by 2075 under a worst-case scenario devoid of<\/em>
mitigation measures.<\/em><\/a><\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure 3:<\/strong> CMIP6 GCM ensemble mean simulations spanning from 1850 to 2100, employing historical effective radiative forcing functions from 1850 to 2014 (see Figure 1C) and the forcing functions based on the SSP scenarios 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Curve colors are scaled according to the equilibrium climate sensitivity (ECS) of the models. The right panels depict the risks and impacts of climate change in relation to various global Reasons for Concern (RFCs) (IPCC, 2023). (Adapted from Scafetta, 2024).<\/em><\/p>\n\n\n\n

Conclusion<\/strong><\/em><\/h4>\n\n\n\n

Over the span of approximately three decades, from the publication of the First Assessment Report (FAR, IPCC, 1990) to the Sixth Assessment Report (AR6, IPCC, 2021), the Intergovernmental Panel on Climate Change (IPCC) has significantly advanced  <\/del>marked up its understanding of the role of anthropogenic emissions in driving global warming.<\/strong><\/em><\/p>\n\n\n\n

In the 1990s<\/strong> the IPCC posited that both natural<\/strong> mechanisms and human<\/strong> activities could have contributed roughly equally (\u223c50% each)<\/strong> to the observed warming of the 20th century. However, since<\/strong> the years 2000s<\/strong> the prevailing scientific opinion has shifted, and the IPCC (AR6, 2021) now asserts that human activities are almost exclusively responsible (\u223c100%)<\/strong> for the global warming and climate change observed from 1850\u20131900 to 2011\u20132020.<\/em><\/p>\n\n\n\n

The most recent assessment reports IPCC<\/strong> (2021, 2023) underscore this conclusion with striking clarity. As shown in Figure 2, the average contribution of natural factors \u2014 solar and volcanic forcing and internal natural variability<\/strong> \u2014 to global warming during the aforementioned period is estimated to be approximately 0\u00b0C. <\/strong> Consequently, from the CMIP GCM perspective, concerns about future climate warming due to additional anthropogenic greenhouse gas (GHG) emissions are well-founded. However, this conclusion depends on the reliability of global surface temperature records and the robustness of the physical science underpinning global climate models (GCMs).<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The findings outlined above underscore significant uncertainties<\/strong> in climate modeling, climate data, solar records, and solar-climate interactions, leaving unresolved<\/strong> the key question of whether observed warming <\/strong>is primarily driven by anthropogenic factors, natural processes, or their interplay.<\/strong> Empirical methodologies, such as those utilized by Scafetta (2023a, 2024) and Connolly et al. (2023), highlight this ongoing ambiguity.<\/em><\/p>\n\n\n\n

Concerns are mounting regarding the limitations of the CMIP GCMs<\/strong> employed by the IPCC in its assessment reports from 2007, 2013, and 2021. These models appear unable to accurately replicate natural climate variability<\/strong> across different timescales, highlighting critical unresolved issues in fundamental climate dynamics.Also the magnitude of solar variability across temporal scales requires further investigation, particularly given the strong correlations identified between solar proxy records and climate patterns throughout the Holocene.<\/strong> Schmutz (2021) argued that such strong correlations challenge the validity of the low-variability TSI models, such as those proposed by Matthes et al. (2017), Kopp et al., 2016 and Wu et al. (2018). Since these models serve as solar forcing inputs for the CMIP6 GCMs, their choice needs to be reconsidered.<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Climate science remains far from settled, yet trillions of dollars continue to be allocated<\/strong>\u00a0toward policies aimed at mitigating extreme hypothetical warming scenarios based on potentially flawed GCM outputs. Historically, atmospheric CO2 levels have been 10 to 20 times higher than current concentrations during approximately 95% of Earth\u2019s history since complex life emerged 600 million years ago (Davis, 2017). Notably, CO2 concentrations often lag temperature changes across different timescales, suggesting\u00a0temperature fluctuations may drive CO2 variations rather than vice versa<\/strong>\u00a0(Shakun et al., 2012; Koutsoyiannis, 2024).<\/em><\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

Advancing climate science requires directly confronting<\/strong>\u00a0uncertainties<\/strong>\u00a0in detection, attribution, and modeling. Further research on unresolved issues is critical for improving climate risk assessment and developing more effective strategies for addressing future environmental challenges.<\/em><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The Coupled Model Intercomparison Project (CMIP) global climate models\u00a0(GCMs) assess\u00a0that nearly\u00a0100% of global surface warming\u00a0observed\u00a0between 1850\u20131900 and 2011\u20132020 is attributable to\u00a0anthropogenic drivers like\u00a0greenhouse gas emissions.\u00a0These models\u00a0also generate future climate projections based on shared socioeconomic pathways (SSPs), aiding in risk assessment and the development of costly \u201cNet-Zero\u201d climate mitigation strategies.<\/p>\n","protected":false},"author":121246920,"featured_media":264182,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691818056,691818153,691822921,691818154],"class_list":{"0":"post-385343","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-uncategorized","8":"tag-climate-change","9":"tag-climate-models","10":"tag-equilibrium-climate-sensitivity-ecs-2","11":"tag-net-zero","13":"fallback-thumbnail"},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=2560%2C1920&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1Cfd","jetpack-related-posts":[{"id":440719,"url":"https:\/\/climatescience.press\/?p=440719","url_meta":{"origin":385343,"position":0},"title":"Climate Models’ Fatal Flaws Exposed: Major Open Issues in Detection, Attribution, and Projections","author":"uwe.roland.gross","date":"21\/04\/2026","format":false,"excerpt":"Italian physicist Nicola Scafetta has long argued that IPCC climate models (the CMIP ensembles used in AR6 and updates) underestimate solar influences and natural variability while over-relying on greenhouse gas (GHG) forcing. 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A closer look at Nicola Scafetta's new\u00a0study\u00a0shows that climate models repeatedly fail to\u2026","rel":"","context":"In \"CMIP6 climate model\"","block_context":{"text":"CMIP6 climate model","link":"https:\/\/climatescience.press\/?tag=cmip6-climate-model"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/0-Ma.jpg?fit=1200%2C664&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/0-Ma.jpg?fit=1200%2C664&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/0-Ma.jpg?fit=1200%2C664&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/0-Ma.jpg?fit=1200%2C664&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/0-Ma.jpg?fit=1200%2C664&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":197316,"url":"https:\/\/climatescience.press\/?p=197316","url_meta":{"origin":385343,"position":2},"title":"AR6 Model Failure Affirmed: \u2018No Model Group Succeeds Reproducing Observed Surface Warming Patterns\u2019","author":"uwe.roland.gross","date":"25\/04\/2022","format":false,"excerpt":"A\u00a0new study\u00a0published in\u00a0Geophysical Research Letters\u00a0highlights the abysmal model performance manifested in the latest Intergovernmental Panel on Climate Change report (AR6). The 38 CMIP6 general circulation models (GCMs) fail to adequately simulate even the most recent (1980-2021) warming patterns over 60 to 81% of the Earth\u2019s surface. 0Geophysical-Research-Letters-2022-Scafetta-Advanced-Testing-of-Low-Medium-and-High-ECS-CMIP6-GCM-Simulations-VersusHerunterladen Dr. Scafetta places\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/04\/0Screenshot-2022-04-25-211714.png?fit=801%2C832&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/04\/0Screenshot-2022-04-25-211714.png?fit=801%2C832&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/04\/0Screenshot-2022-04-25-211714.png?fit=801%2C832&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/04\/0Screenshot-2022-04-25-211714.png?fit=801%2C832&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":223353,"url":"https:\/\/climatescience.press\/?p=223353","url_meta":{"origin":385343,"position":3},"title":"Satellite Temperature Data Show Almost All Climate Model Forecasts Over the Last 40 Years Were Wrong","author":"uwe.roland.gross","date":"11\/10\/2022","format":false,"excerpt":"A major survey into the accuracy of climate models has found that almost all the past temperature forecasts between 1980-2021 were excessive compared with accurate satellite measurements.","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/07\/0Global-Temperature-Models.png?fit=576%2C402&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/07\/0Global-Temperature-Models.png?fit=576%2C402&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/07\/0Global-Temperature-Models.png?fit=576%2C402&ssl=1&resize=525%2C300 1.5x"},"classes":[]},{"id":426316,"url":"https:\/\/climatescience.press\/?p=426316","url_meta":{"origin":385343,"position":4},"title":"Why Rethinking Climate Change\u2013Nicola Scafetta","author":"uwe.roland.gross","date":"15\/02\/2026","format":false,"excerpt":"A central theme is natural climate variability. Across the Holocene\u2014the last 11,700 years\u2014the climate system exhibited a Climate Optimum (6,000\u20138,000 years ago) and repeated oscillations: multidecadal cycles, centennial fluctuations, and millennial-scale reorganizations.","rel":"","context":"In \"Anthropogenic Effects\"","block_context":{"text":"Anthropogenic Effects","link":"https:\/\/climatescience.press\/?tag=anthropogenic-effects"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/AQM9t0sOdtS_CQmr58yHizif319rVrBVVVwEYoXIRlGK080T40VUD0xKdGJwUk15yYx5KrVYFe6_hCpO2Jca_8CI0tjfoKgPphAny1raoc5UMGsFFUm7Iz0oCN20x_a8.jpeg?fit=1200%2C713&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/AQM9t0sOdtS_CQmr58yHizif319rVrBVVVwEYoXIRlGK080T40VUD0xKdGJwUk15yYx5KrVYFe6_hCpO2Jca_8CI0tjfoKgPphAny1raoc5UMGsFFUm7Iz0oCN20x_a8.jpeg?fit=1200%2C713&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/AQM9t0sOdtS_CQmr58yHizif319rVrBVVVwEYoXIRlGK080T40VUD0xKdGJwUk15yYx5KrVYFe6_hCpO2Jca_8CI0tjfoKgPphAny1raoc5UMGsFFUm7Iz0oCN20x_a8.jpeg?fit=1200%2C713&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/AQM9t0sOdtS_CQmr58yHizif319rVrBVVVwEYoXIRlGK080T40VUD0xKdGJwUk15yYx5KrVYFe6_hCpO2Jca_8CI0tjfoKgPphAny1raoc5UMGsFFUm7Iz0oCN20x_a8.jpeg?fit=1200%2C713&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/02\/AQM9t0sOdtS_CQmr58yHizif319rVrBVVVwEYoXIRlGK080T40VUD0xKdGJwUk15yYx5KrVYFe6_hCpO2Jca_8CI0tjfoKgPphAny1raoc5UMGsFFUm7Iz0oCN20x_a8.jpeg?fit=1200%2C713&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":223011,"url":"https:\/\/climatescience.press\/?p=223011","url_meta":{"origin":385343,"position":5},"title":"Satellite Temperature Data Show Almost All Climate Model Forecasts Over the Last 40 Years Were Wrong","author":"uwe.roland.gross","date":"09\/10\/2022","format":false,"excerpt":"Maybe a climate model with no \u2018ECS\u2019 factor could do better? But anything that smacks of natural variation inevitably faces resistance from climate alarm promoters.","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-433.png?fit=763%2C605&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-433.png?fit=763%2C605&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-433.png?fit=763%2C605&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-433.png?fit=763%2C605&ssl=1&resize=700%2C400 2x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/385343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/users\/121246920"}],"replies":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=385343"}],"version-history":[{"count":7,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/385343\/revisions"}],"predecessor-version":[{"id":385361,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/385343\/revisions\/385361"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/media\/264182"}],"wp:attachment":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=385343"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=385343"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=385343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}