In science, what we don’t know often matters just as much as what we do. But just as important—and less often acknowledged—is what we choose not to ask. In climate science, certain findings are repeated so often they begin to feel like settled truth. Others fall away from focus, rarely explored, or gradually left out of public discussion. The shape of the narrative isn’t only determined by evidence. It’s shaped, too, by omission.
The climate system resists simplification. It’s vast, dynamic, and only partly observable. While some areas are well studied, others remain uncertain—either because they’re difficult to measure, or because they no longer fit the prevailing storyline. That doesn’t mean they’re unimportant. But it does mean they’re often underrepresented, especially in public-facing summaries of the science.
The historical temperature record is one of the most fundamental datasets in climate science—and one of the most constrained. Modern, reliable surface measurements only extend back about 150 years, and even those early records are uneven in coverage and quality. For earlier periods, scientists rely on proxy data: tree rings, ice cores, corals, sediments. These help reconstruct past climates, but they are not direct measurements. They involve interpretation, calibration, and statistical modeling. Different methods sometimes yield different pictures. When modern warming is described as “unprecedented,” that conclusion rests on reconstructions with real uncertainty. That uncertainty doesn’t erase the trend—but it should shape how confidently we present it.
Climate models are another place where evidence meets assumption. These models are sophisticated and useful, but they are not experiments in the traditional sense. They are tools—simulations of a system we do not fully understand, built using inputs we can’t always measure directly. To work, they simplify processes like cloud dynamics, surface fluxes, and ocean circulation. They are often tuned to match historical trends. And when they diverge from observed data—as they sometimes do—the gap is typically downplayed. But those divergences are not distractions. They may point to mechanisms we’ve missed, or interactions we’ve underestimated. In a healthy scientific culture, model mismatch would prompt more questions, not fewer.
There are also entire areas of the climate system that receive far less attention than they arguably deserve. The role of solar variability is one. While total solar output has been relatively stable in recent decades, the sun’s influence on Earth is not purely a matter of wattage. Solar cycles affect ultraviolet radiation, magnetic activity, and possibly even cloud formation through cosmic ray modulation. These are not speculative ideas—they’ve been studied for decades—but they rarely feature in mainstream climate messaging. That absence isn’t always deliberate. Sometimes, fields develop momentum, and certain avenues of inquiry lose institutional support. But if we say the science is “settled,” we should be clear about which parts have been explored thoroughly, and which still carry real uncertainty.
Other overlooked influences are more human in origin. From the 1940s through the early 1960s, nuclear weapons tests injected dust and aerosols into the upper atmosphere on a global scale. These events were brief, but they altered atmospheric composition and circulation in ways that have not been fully revisited. Likewise, documented efforts at weather and climate modification—cloud seeding, stratospheric aerosol trials, and geoengineering experiments—date back over half a century. Projects like Project Stormfury and early U.S. military cloud manipulation attempts are part of the historical record. Their scale was modest, but they reflect a wider pattern: the human influence on climate extends beyond carbon emissions. That’s not a reason for alarm, but it is a reason to include them in the picture.
Land use changes are another important driver often relegated to the background. Agriculture, irrigation, deforestation, and urbanization all affect how the land interacts with sunlight, moisture, and wind. These changes alter albedo, increase surface roughness, and shift water cycles. Cities, for example, routinely produce their own microclimates through the urban heat island effect. These local shifts don’t just stay local—they feed back into regional weather and atmospheric circulation. Yet they are rarely emphasized, perhaps because they don’t map neatly onto the carbon framework that dominates climate dialogue. Still, their influence is real and deserves more than a footnote.
The oceans, too, are central to climate—and deeply undermeasured until very recently. Most of the heat trapped by Earth’s energy imbalance ends up in the oceans. But global-scale subsurface temperature records only became available in the early 2000s with the rollout of the Argo float system. Before that, observations were sparse and uneven. Even today, estimates of long-term ocean heat content involve large uncertainties. This matters, because if we don’t know how heat is distributed below the surface, we may misread how the planet is actually responding to radiative forcing.
Beyond the data gaps, the oceans also give rise to patterns of natural variability that complicate attribution. Large-scale oscillations like El Niño, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation emerge from internal dynamics and can shift global climate patterns for years or even decades. These cycles don’t require any human trigger, and they can temporarily mask or amplify long-term trends. That makes attribution studies—especially ones trying to assign percentages of cause—much more difficult. The result is that many attribution claims rely on probabilities, not direct evidence. That distinction is rarely made clear to the public. But it matters.
To point out these gaps is not to argue against action. It is to argue for clarity. The science of climate is still evolving. That’s not a flaw—it’s how science works. But when gaps are ignored, and complexity is flattened to fit a narrative, public trust begins to fray. And when skepticism arises, it’s often blamed on ignorance or denial. Sometimes, though, it’s simply a rational response to mixed signals and missing context.
None of this means we should do nothing. It means we should stop pretending we already know everything. When we overstate our confidence, we encourage backlash. When we simplify the science to fit the politics, we invite doubt. The better path is not more certainty—it’s more honesty.
We cannot eliminate every unknown. But we can choose to treat uncertainty as part of the process, not as a threat. If climate science is to remain credible—not just among experts, but with the public—it must resist the temptation to close questions too quickly. Some of the most important truths begin in the gaps.